dataesr/scientific-paragraphs-categorization huggingface dataset:
Satellite Image Processing Glacier displacements were derived from the cross correlation of optical images through a two-step routine: the Coregistration of Optically Sensed Images and Correlation software package (COSI-Corr)  [Leprince et al. , 2007]. This is a free plug-in for the ENVI software. The first step produces a fine orthorectification of the raw satellite image. This procedure requires a base image (master: Landsat), a raw image with its ancillary data (slave: Level 1A ASTER scenes), and a DEM (e. g. , SRTM). An initial set of tie points is manually selected on stable areas, converted to ground control points, and optimized by the software through cross correlation. All the raw slave images are then accurately orthorectified and coregistered with a subpixel accuracy. ASTER band 2 and LANDSAT TM band 3 which are on the 0.
Data Extraction The outline and the central flow line of the glacier (Figure  1a ) were hand digitized on the 2007 ASTER scene. Visual interpretation and hand digitizing are standard procedures for mapping debris-covered ice which cannot be accurately identified with automatic methods  [Paul et al. , 2013]. We consider the technique to be accurate within 1 pixel. The origin of the flow line is assumed to be the base of the south face of Mount Aconcagua, while the position of the snout for the last 20 years was 8. 2 km away. Velocities and elevation changes were extracted along the central flow line. Elevation changes are reported in meters as mean values ± standard deviation along the center flow line in the reservoir or receiving zone.
This showed that the DEMs are mostly unbiased with a mean elevation difference of À0. 5 m ± 14 m (Figure  2b ). The elevation changes along the flow line relative to February 2000 are shown in Figure  4. In the buildup phase between February 2000 and January 2003, the average elevation change in the reservoir zone was 8 ± 7 m, but the data are limited. The quality of the 13 January 2001 ASTER DEM is poor, and no other topographic information is available for that period. The elevation data are worse in the upper 3. 6 km, where snow cover is common, and also in the area immediately in front of the glacier snout (after 8. 2 km). The latter is probably due to the fact that the flow line is located in a narrow valley (250 m wide) with steep walls (30 m), where the shading is strong.
Acknowledgments The data for this paper are available by contacting the corresponding author. The ASTER images and DEM used in this paper were available through the GLIMS project  [Kargel et al. , 2014] , for which IANIGLA-CONICET is a regional center. We would like to thank Bruce Raup, Jeff Kargel, and Silvia Delgado who fostered this collaboration. Images and DEMs were downloaded from the USGS Global Visualization Viewer (http://glovis. usgs. gov/) which is one of the Earth Resources Observation and Science Center sites. We thank Fabián Díaz and Pablo Lizana for field data and photos and the Parque Provincial Aconcagua for granting access to the HI. We are grateful to the Editor, the Associate Editor, and the three anonymous reviewers who provided many insightful comments to improve this manuscript.
RAMSES RAMSES is a code for simulating the formation of large scale structures, galaxy formation and self-gravitating hydrodynamics in general  (Teyssier 2002). Collisionless N-body dynamics are solved via a particle-mesh integrator. The adaptive mesh refinement was turned off in this run (see Sec. for more explanations on this). Gas dynamics is modeled using a second-order unsplit Godunov scheme  (Teyssier et al. 2006; Fromang et al. 2006 ) based on the HLLC Riemann solver  (Toro et al. 1994). We assume a perfect gas Equation of State (hereafter EoS) with γ = 5/3. We consider star formation using a phenomenological approach. In each cell with gas density larger than a gas overdensity δ = 50, we spawn new star particles at a rate given by ρ = ρgas t f f with t f f = 3π 32Gρ (1) where t f f is the free-fall time of the gaseous component and = 0.
We refrain from using this approach, because these subgrid models account for star formation and radiation from massive stars in star forming complexes, which are already explicitly accounted for in our feedback models. Therefore we set the RAMSES parameters so that the polytropic EoS does never kick in. To summarize, we used for this simulation rather standard galaxy formation recipes, which have proven quite successful in reproducing various properties of galaxy evolution  (Ocvirk et al. 2008; Governato et al. 2009 Governato et al. , 2010)). The main novelty of this study is the inclusion of the coupling between the hydrodynamics and the radiation produced by the stars, treated by our radiative transfer module ATON, described in Sec. 2. 1. 2. At the galactic scale, since galaxies can be smaller than one CoDa cell, their ISM is not resolved.
Radiation-hydrodynamics coupling We developed an interface that enables data exchange between RAMSES and ATON on the fly, leading effectively to a coupling between dynamics (handled by RAMSES) and radiation (handled by ATON). At the end of a dynamical time-step, RAMSES sends to ATON the gas density, temperature and ionized fraction. Radiative transfer is then performed by ATON using these inputs, via sub-cycling of 100s to 1000s of radiative substeps. Once this sub-cycling is completed, ATON sends the temperature and ionized fraction back to RAMSES. However, CUDATON (the GPU port of ATON) can only handle regular Eulerian grids. For this reason, AMR is not used for this simulation, and RAMSES is effectively used as a hydro-PM code. Furthermore, CUDATON assumes a Cartesian domain decomposition for the MPI-parallelism when similar domains are treated by each GPU.
This is quite unique among the handful of existing radiation-hydrodynamics codes. Some of them, mostly those involving ray-based photon propagation schemes, use infinite speed of light. Others, in particular those using moment-based methods such as ATON for the RT (e. g. RAMSES-RT), or  Gnedin & Kaurov (2014) , use the reduced speed of light approximation (as low as 1/100 of the real speed of light) in order to reduce the computational cost of the simulation. This is because in such a framework, the time-step of the code is set by the fastest physical process (gravitational, hydrodynamical and radiative). Since the speed of light is typically 100 to 1000 times faster than the speed of sound or bulk matter motions in cosmological simulations, the time-step must be about 100's of times shorter and therefore the simulation will be 100's of times slower than its pure hydrodynamical counterpart.
The resulting map is shown in the right panel of Fig. 4. Thanks to the mass-weighting, both sources and sinks appear. While the largest gas over-densities seem to host sources, the smaller ones tend to be dark and act as sinks. Interestingly, most haloes hosting sources seem to be also partly sinks, and the mass-weighted neutral fraction map shows that all photon sources are located next to rather neutral cells. This illustrates how galaxies are all at least marginally resolved in CoDa, i. e. even when they are just a few cells across, the code can still capture, albeit coarsely, their strongly heterogeneous internal structure. A filament of gas joins the 2 main sources of the map. The lower half of it consists of a chain of small gas overdensities, and seems able to self-shield, as shows the photondensity map.
This difference in methodology makes a direct comparison with  Gnedin & Kaurov (2014)  difficult. Moreover, their treatment of star formation relies on modelling molecular hydrogen formation. This raises the density threshold for star formation, and therefore pushes star formation to higher mass haloes. The smallest galaxies, which are the ones most-strongly suppressed by radiative feedback in CoDa, would probably not have formed any stars at all within the CROC formalism. Therefore the question of the impact of radiative feedback on low mass haloes is tied, not only to spatial and mass resolution, but also to the choices made for the star formation recipe. While performing a higher resolution CoDa would be prohibitively expensive with the current code and facilities, we performed a simple resolution study spanning 2 times higher and up to 4 times coarser spatial resolution with respect to CoDa, using 512 3 boxes of 4 to 32 h -1 Mpc on a side.
Details can be found in Sec. B. Preliminary results indicate that degrading resolution beyond CoDa's produces a radiative suppression of star formation at increasingly higher masses. However, the test boxes at CoDa resolution and 2 times higher resolution yield the same suppression mass scale of about ∼ 2 × 10 9 M. We note that this is compatible with the results of  Pawlik et al. (2015) , obtained with a mass resolution comparable to CoDa but better spatial resolution due to the use of an SPH-based Lagrangean hydro code instead of CoDa's unigrid scheme. Their study reports a SFR suppression mass similar to ours, of ∼ 10 9 M.
CONCLUSIONS CoDa (Cosmic Dawn) is a very large, fully coupled radiationhydrodynamics simulation of galaxy formation in the local universe during the Epoch of Reionization. It was performed on Titan at Oak Ridge National Laboratory using RAMSES-CUDATON deployed on 8192 nodes, using 1 core and 1 GPU per node. This is the first time a GPU-accelerated, fully coupled RHD code has been used on such a scale. The simulation accurately describes the properties of the gas and its interplay with ionizing radiation, in particular the growth of typical butterfly-shaped ionized regions around the first stars and first galaxies, accompanied by photo-heating and the subsequent progressive smoothing of small scale gas structures. Gas filaments tend not to be selfshielded once the reionization radiation sweeps across them; the flux density of the ionizing radiation internal to these filaments thereafter is identical to that of the background.
ACKNOWLEDGEMENTS This study was performed in the context of several  French ANR  ( Agence Nationale de la Recherche ) projects. PO acknowledges support from the  French ANR  funded project  ORAGE  ( ANR-14-CE33-0016 ). NG and DA acknowledge funding from the  French ANR  for project  ANR-12-JS05-0001  (EMMA). The CoDa simulation was performed at  Oak Ridge National Laboratory / Oak Ridge Leadership Computing Facility  on the Titan supercomputer (INCITE 2013 award  AST031 ). Processing was performed on the Eos, Rhea and Lens clusters. Auxiliary simulations used the  PRACE - 3IP  project ( FP7 RI-312763 ) resource curiehybrid based in France at  Très Grand Centre de Calcul. ITI was supported by the  Science and Technology Facilities Council  [grant number  ST/L000652/1 ]. SG and YH acknowledge support by  DFG  grant  GO 563/21-1.
YH has been partially supported by the  Israel Science Foundation  ( 1013/12 ). AK is supported by the  Ministerio de Economía y Competitividad  and the  Fondo Europeo de Desarrollo Regional (MINECO/FEDER, UE) in Spain  through grants  AYA2012-31101  and  AYA2015-63810-P  as well as the  Consolider-Ingenio 2010 Programme  of the  Spanish Ministerio de Ciencia e Innovación (MICINN)  under grant Mul-tiDark  CSD2009-00064. He also acknowledges support from the  Australian Research Council (ARC)  grant  DP140100198. GY also acknowledges support from  MINECO-FEDER  under research grants  AYA2012-31101  and  AYA2015-63810-P. PRS was supported in part by  U. S. NSF  grant  AST-1009799 ,  NASA  grant  NNX11AE09G ,  NASA/JPL  grant RSA Nos. 1492788  and  1515294 , and supercomputer resources from  NSF   XSEDE grant   TG-AST090005  and the  Texas Advanced Computing Center (TACC)  at the  University of Texas at Austin.
Using a direct solver with parallelization over 4 nodes, a classical identification process with mCRE would require about 4 h for this problem. Coupled with PGD, this same process takes only 5 min in the online phase (and additional 30 min to compute PGD solutions with 15 modes in the offline phase). All computations were performed with a Python FE code using the scipy. sparse module for matrix representation, and systems were solved with a dedicated direct solver based on the UMFPACK library. The speed-up thus comes from the difference between the original mCRE and the PGD-mCRE strategies. A crude complexity analysis of the two approaches can be conducted as follows: 0 1 0 2 0 -0. 2 -0. 1 0 0. 1 p ext X u 1,i u 1,i 0 5 1 0 0 5 • 10 -2 0. 1 0. 15 p inc X u 2,i u 2,i 0 5 1 0 0 5 • 10 -2 0. 1 σ r κ u i κ u i 0 1 0 2 0 -0.
In contrast with adults [e. g. , Ref. (37) ], EMA has not yet been assessed, to our knowledge, for studying the relationship between psychiatric symptoms and patterns of substance use in adolescents. In addition, the feasibility of using EMA in adolescents with severe psychiatric symptoms remains understudied. The smartphone app Stop-Cannabis was developed in 2013 in Geneva, Switzerland with financial support from the local Health Department. 1  The app aims to help users reduce or stop their cannabis use and prevent relapse. Stop-Cannabis consists of several modules based on cognitive behavioral therapy strategies, motivational interviewing, and relapse prevention programs. Particular effort has been made to include the principles of selfdetermination (autonomy, competence, and relatedness)  (38)  into the app and promote its customization  (39).
As in other studies  (40) (41) (42) , participant interaction and rewards support the playful aspect of the intervention. Personalization of the users' goals and the app main screen may further contribute to increase adoption by user  (43). The contents of the application are summarized below [and described in detail in Ref. (39) ]. Since February 2013, the Stop-Cannabis app [available on Google Play (Android) and (iOS) App Store] has been downloaded more than 13,000 times, and the app is currently used by 1,000 people every month. The app was reported to have a good level of acceptability among community users in an online satisfaction survey  (39). This study is part of a general collaborative research program between the child and adolescent psychiatric department team and the addiction unit.
Ecological Momentary Assessment The EMA was designed as an app for mobile phones. This application allows several daily self-assessments (e. g. , five times a day for 2 weeks at different times of the study/monthly). Data are collected in response to a signal from the EMA app that occurs at various times of the day. The app has been programed to send signals (i) a fixed number of times per day at predetermined times (e. g. , at bedtime) and (ii) at random (with a balance between week, weekends, and holidays, with different time intervals in daytime). Participants are also asked to complete self-assessment immediately following substance use. The same questions are presented regardless of assessment type. There are seven questions in total: • Information relating to substance use:  Daily use of each substance in list.
• Information relating to environment:  Stressful events.  Activities: physical activity, planned activity, lone activity, social activity with peers, family activity, and online activity. Participants will be trained on EMA use during the week prior to hospital discharge. They will be asked to respond to any signals within 1 h if possible. Consistent with other EMA protocols, participants will complete 2 days of practice data (not used for analyses), after which they will receive feedback on adequate use and compliance. Participants will then be invited to complete EMA assessments for 2 weeks, as this timeframe appears sufficient to monitor substance use  (45). To limit attrition, participants will be paid 30€ for completing the baseline assessment and 10€ for each week of complete EMA data, up to a maximum possible payment of 60€ over 1 year.
The Stop-Cannabis App Launched in 2013, Stop-Cannabis is available free on iOS and Android. It has been continuously updated and improved over the past 3 years, in response to users' suggestions. When people use the app for the first time, they are asked to document their objectives and choose a quitting date. The app has several modules. First, it allows users to assess their cannabis usage profile (including motivations for use and severity of cannabis use) via different assessments  (49). Users then receive an individually tailored feedback message that includes links to online psychoeducation and complementary resources. Second, motivational messages are regularly delivered using push-notification technology at different stages of the quitting process, on the basis of updated information regarding current substance use, the selected quitting date, and exposure to situations associated with a high risk of relapse documented by high levels of craving or irritability.
A Unique Device Identifier (UDI) is used to connect the participants' device to the study database and will be replaced in the data set with a random ID for anonymity. After study completion, the UDIs will be automatically deleted from users' devices. The app will be available for participants in the intervention group for 12 months. After this 2 http://www. stop-cannabis. ch period, the app will automatically stop data collection. However, participants are free to use the app for as long as they want.
Variables and Instruments Table  1  lists the questionnaires and other assessments that will be used at baseline and at follow-up. The reading level of the questionnaires is grade 6. The questionnaires we plan to use to measure addiction are described below. A combination of self-and clinician-report measures with different time scales (i. e. , DEP-ADO, 12 months; CAST, 6 months; RECAP, 1 month) will be used to examine whether EMA data are consistent with information collected in the traditional way. The second type of measures relate to psychopathology with the aim of examining the interplay between substance use and psychiatric disorders.
Primary Outcomes Pearson correlations will be used to determine the strength of associations between daily substance use assessed by either EMA, or self-report questionnaire (e. g. , CAST), or by clinician-report questionnaire (e. g. , RECAP) during the clinical assessment at 6 months. Clinical reliability of the EMA will be endorsed if the correlations are statistically significantly higher than 0. 70. Further analyses would be performed to estimate different aspects of reliability of the data collected in line with Perrez et al. (64). The severity of SUDs at baseline should predict the severity of substance use and the level of functional impairment at 1 year at least to comparable levels while using EMA data at baseline compared with traditional methods. Two simple linear regression models will be performed.
For both the models, the dependent variable will be the severity of SUDs at follow-up; in one model, the independent variable will be the severity of SUDs based on EMA data (Model A), whereas in the other model, the variable will be severity of SUDs based on self-and clinician-report (Model B). The estimate of the independent variable is expected not to be lower in the Model A than in the Model B. The dropout rate is expected to be less than 40% at 6 months and less than 50% at 12 months for both the EMA and traditional method, with no significant difference between methods. The results of the satisfaction survey will be estimated by analyzing the percentage of answers and the differences among the different categories using Chi-square analysis.
In particular, we will test the mediation effect of the baseline impulsivity score, the baseline level of sensationseeking (subscales of the UPPS-P score), and the baseline level of mood lability (ARI total score) on the relationship between psychopathology and SUDs. (4) Comparable analyses would be conducted to determine the clinical and psychosocial risk factors for the persistence of SUDs among those with non-cannabis substance use at baseline. (5) Specific analyses based on mixed-effect models (a random effect for participants and fixed effects for time) will be performed to examine the interplay between the pattern of substance use, psychiatric symptoms, and contextual stressors for treating EMA data [as in Ref. (45, 66) ]. In line with previous recommendations, data will be studied at the daily level (with average ratings), at the concurrent momentary level, and at the prospective level using both linear and non-linear modeling  (32).
Ecological momentary assessment data provide insights on the sequence between substance use, contextual factors, and psychiatric symptoms that could not have been obtained using more traditional self-report measures. This is particularly important for adolescents where psychopathology may be more fluctuating and context-dependent than in adults. In contrast with previous EMA data research projects conducted in children and adolescents, our sample encompasses a large range of psychiatric disorders with a more severe profile. Finally, in line with other studies (72), the insights obtained from EMA may not only serve researchers and clinicians but might also benefit patients directly as patients become increasingly aware of their symptoms. Finally, this research will provide information that ultimately helps determine whether a randomized controlled trial could be conducted in the future to test the benefit of a digital intervention based on the Stop-Cannabis app in adolescents with severe comorbid psychiatric disorders.
EtHiCS and diSSEMination informed Consent and institutional review Boards Regarding patient safety, all participants will receive detailed information about local help in case of crisis, emergency, or suicidal ideation. Participants will also be informed about, and trained how to use, the apps and told what can be expected from their use. They will also be advised to consult their doctor and share concerns with their family and relatives according to their needs. Participants will be instructed to not use the apps when it is inconvenient (e. g. , in class) or unsafe (e. g. , on bikes). Informed consent will be obtained from the adolescent and his/her family or legal representatives after they have received information about the study objectives and procedures. Agreement has already been provided by the scientific board of the Institute of Research in Public Health; however, data collection will only start when the protocol will be approved by the Ethics Committee (EC) of the Pitié-Salpêtrière Hospital.
Salariés (CNAMTS), la Mission interministérielle de lutte contre les drogues et les conduites addictives (MILDECA), and l'Observatoire national des Jeux (ODJ) on the basis of an open invitation to tender from the IreSP in 2015 (reference "IReSP-15-Prevention-11"). The study protocol has been recorded on the European Clinical Trials Database (EudraCT Number 2016-001999-30).
6 m). Second, a jump increase in HY-2 H s values was detected starting in April 2013, which was associated with the switch to backup status of the HY-2 sensors and the subsequent update of its data processing software. Although reported by previous studies, these two deficiencies had not been accounted for in calibrations. Therefore, the HY-2 wave height records are now subdivided into two phases (time periods pre-and post-April 2013) and a two-branched calibration is proposed for each phase. These revised calibrations, validated throughout the range of significant wave heights of 1-9 m, are expected to improve the practical applicability of HY-2 H s measurements significantly.