dataesr/scientific-paragraphs-categorization huggingface dataset:
Most importantly, its derivative is a modified q-Weibull distribution  [24] , dTTY f it dE MBq µAh = MAX        0; c (E -E thr ) exp        -(E -b) 2 a 2               which reflects the global shape of the (p,n) and (p,2n) excitation functions, commonly used in the field of the production of medical radioisotopes. The request TTY exp (E thr ) = 0 provides the condition d = a 2 c 2 exp        -(b -E thr ) 2 a 2        and limits the number of TTY fit parameters to 3: a, b and c. Once those parameters are obtained, the cross-section values can be estimated as σ(E)[mb] = τ[h]Ze[C]m[u] N A H • dTTY f it dE MBq µAh • dE dx MeV mg/cm 2 •10 42 In our case, TTY measurements were obtained on CaCO 3 targets instead of metallic Ca. Therefore, we used dE/dx(E) values corresponding to the energy loss in calcium carbonate (provided by SRIM software  [25] ), m = 100 u to address the mass of CaCO 3 , and H as the level of enrichment of employed material.
We have also calculated the 95% confidence band for TTY fit (E) fit and reconstructed the cross-section. Details of our calculations are shown and explained in the Python code attached to this paper. Alternatively, in  [21] , the cross-section was reconstructed after fitting the TTY curve by calculating target yields (TY) for thicknesses corresponding to 0. 1 MeV projectile energy loss each 1 MeV and multiplied by projectile range. This method assumes the constant stopping-power in each layer. In our approach, this simplification was not necessary.
Results and Discussion In Figures  1 2 3 4 5 , we show the TTY data and the reconstructed cross-sections for 43 Ca(p,n) 43  Sc,  44 Ca(p,n) 44g Sc, 44 Ca(p,n) 44m Sc, 48 Ca(p,2n) 47 Sc and 48 Ca(p,n) 48 Sc reactions (the fit parameters are shown in Table  2  while the reconstructed cross-section values are listed in Table  3 ). We compare them with the experimental cross-section in  [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] , with the recommended values from  [36] , with the predictions of the EMPIRE [37] evaporation code (version 3. 2. 2 Malta) and with the TENDL-2017 cross-section library  [38]. All reconstructions exhibit a similar shape to the model predictions and measured cross-section values, indicating the validity of modified q-Weibull distribution in estimating the global shape of the (p,n) and (p,2n) excitation functions.
Conclusions and Summary We have presented an attempted numerical method for cross-section evaluation based on the thick target yield (TTY) measurements obtained from the irradiation of thick targets (in which the energy of a projectile is reduced to the reaction threshold). This method is based on fitting a function with three free parameters to TTY data points and using its analytical derivative to obtain the cross-section. The fitting requires the knowledge of the reaction threshold and a sufficient number of experimental points to represent the shape of the TTY curve, including the saturation region. Using this approach, we were able to obtain a useful estimation of cross-sections for the production of medically important 43 Sc, 44g Sc, 44m Sc, 47 Sc, and 48 Sc radioisotopes via (p,n) and (p,2n) reactions on Ca.
The following are available online at http://www. mdpi. com/2410-390X/3/2/29/s1. Author Contributions: M. S. : formal analysis, software, methodology, writing-original draft, writing-review & editing; J. J. : conceptualization; F. H. : supervision, writing-review & editing; T. M. : conceptualization, methodology, supervision, writing-review & editing; K. S. : supervision, writing-review & editing; W. Z. : conceptualization, supervision, writing-review & editing. Funding: Part of this work was performed within the framework of the EU Horizon 2020 project RIA-ENSAR2 (654 002). This research was also partly supported by the Polish Funding Agency NCBiR grant No. DZP/PBS3/2319/2014 and by a grant from the French National Agency for Research called "Investissements d'Avenir", Equipex Arronax-Plus noANR-11-EQPX-0004, Labex IRON noANR-11-LABX-18-01, and ISITE NEXT no.
Supplementary Materials: The following are available online at http://www. mdpi. com/2410-390X/3/2/29/s1. Author Contributions: M. S. : formal analysis, software, methodology, writing-original draft, writing-review & editing; J. J. : conceptualization; F. H. : supervision, writing-review & editing; T. M. : conceptualization, methodology, supervision, writing-review & editing; K. S. : supervision, writing-review & editing; W. Z. : conceptualization, supervision, writing-review & editing. Funding: Part of this work was performed within the framework of the EU Horizon 2020 project RIA-ENSAR2 (654 002). This research was also partly supported by the Polish Funding Agency NCBiR grant No. DZP/PBS3/2319/2014 and by a grant from the French National Agency for Research called "Investissements d'Avenir", Equipex Arronax-Plus noANR-11-EQPX-0004, Labex IRON noANR-11-LABX-18-01, and ISITE NEXT no.
Test data -without target labels -were provided to the participants, who had limited trials of result submissions per competing site. To uphold the quality and validity of research, the individual paper submissions undergo the regular INTERSPEECH peer-review process and have to be accepted for the conference in order to participate in the Challenge. In each Challenge, an acoustic feature set was specified, comprising  384, 1 582, 4 368, and 6 125 attributes, respectively (2009 -2012) , which were obtained by applying statistical functionals to low-level descriptors. For transparency, the openSMILE feature extraction toolkit has been consistently used over the years  (Eyben et al. , 2010 (Eyben et al. , , 2013)) ; openEAR is a release of openSMILE including models for emotion recognition as targeted in the IS09EC Challenge.
Thus, in response to the growing popularity of the Challenge series  (Schuller, 2012)  ComParE 2013 broadened the scope with a larger variety of tasks compared to previous years. In line with INTERSPEECH 2013's theme Speech in Life Sciences and Human Societies, social signals  (Vinciarelli et al. , 2009)  and conflicts in communication  (Roth and Tobin, 2010)  as occurring in real-life were detected and localised. In addition, we re-addressed the topics emotion and intelligibility from IS09EC and IS12STC by introducing new databases and task definitions.
The participants of each call were fully unacquainted and never met faceto-face before or during the experiment. The calls revolved around the Winter Survival Task: The two participants had to identify objects (out of a predefined list) that increase the chances of survival in a polar environment. The subjects were not given instructions on how to conduct the conversation, the only constraint was to discuss only one object at a time. The conversations were recorded on both phones (model Nokia N900) used during the call. The clips were extracted from the microphone recordings of the phones. Thus, clips from the same speaker never overlap, whereas clips from two subjects participating in the same call may overlap (for example in the case of simultaneous laughter). However, they do not contain the same audio data because they are recorded with separate microphones.
Each clip was selected in such a way that it contains at least one laughter or filler event between t = 1. 5 seconds and t = 9. 5 seconds. In total, the database contains 2 988 filler events and 1 158 laughter events. Both types of vocalisation in this database can be considered fully spontaneous. Given this layout, the Social Signals Sub-Challenge introduced for the first time a frame-wise detection and localisation task instead of supra-segmental classification as in the other Sub-Challenges and all previous Challenges. The data were divided into speaker disjoint subsets for training, development, and testing. For trans-   (Young et al. , 2006). Further meta data were not provided. The resulting partitioning by numbers of utterances, number of vocalisation segments (filler, laughter) as well as vocalisation and garbage frames (100 per second) is shown in Table  2.
Child Pathological Speech Database (CPSD) The Autism Sub-Challenge used the "Child Pathological Speech Database" (CPSD)  (Ringeval et al. , 2011) , created at two university departments of child and adolescent psychiatry (Université Pierre et Marie Curie/Pitié-Salpêtière Hospital and Université René Descartes/Necker Hospital), located in Paris, France. The recordings are prompted sentence imitation of 26 sentences representing different modalities (declarative, exclamatory, interrogative, and imperative) and four types of intonations (descending, falling, floating, and rising); another version of this database including emotional speech (CPESD) has been recently studied and released  (Ringeval et al. , 2016; Schmitt et al. , 2016). The CPSD dataset used in the Sub-Challenge comprises 2 542 instances of speech recordings (total duration: 1 h) from 99 children aged 6 to 18 years; 35 of these children show either pervasive development disorders of autism spectrum condition (PDD, 10 male, 2 female), specific language impairment such as dysphasia (DYS, 10 male, 3 female), or PDD non-otherwise specified (NOS, 9 male, 1 female), according to the DSM-IV criteria 2   (First, 1994) , which distinguish ASC subtypes: e.
g. , Autism Disorders (AD), with symptoms in all areas that characterise PDD; or PDD-NOS, which is characterised by social, communicative and/or stereotypical impairments that are less severe than in AD. Further, a monolingual control group of 64 typically developing children (TYP, 52 male, 12 female) is included. None of the TYP subjects had a history of speech, language, hearing or general learning problems  (Demouy et al. , 2011). Typically developing children were recorded in two different places according to their age (middle/high school), whereas children with developmental conditions were either recorded at home or at the clinic (DYS: Necker Hospital, PDD and PDD-NOS: Pitié-Salpêtière Hospital), depending on their availability. Various acoustic conditions are thus present in the data due to the use of different places for the recordings of the children; two different places for TYP, and at least four different places for the three groups of children suffering developmental conditions.
The overall scope of the ComParE 2013 Ideally, we could choose for each year's sub-challenges amongst many database candidates the ones that fit together under a clearly defined umbrella. However, suitable candidates are rather scarce because they have to meet several conditions, i. e. , they have to be new (especially the test set), large enough for experimental purposes, and of considerable interest for the community. Nevertheless, the four sub-challenges in this first ComParE Challenge reflect pivotal aspects of human communication -to be more precise, of specific 'non-communications' and problems of a-typicality, according to the type of speaker and speech phenomenon: • In the SSPNet Vocalisation corpus SVC (social signal sub-challenge), laughter and fillers represent 'non-semantic' phenomena, which are very helpful for characterising speakers and gaining a deeper understanding of dialogues beyond the sole exchange of semantic messages.
They can be modelled and detected together with words, but have been disregarded in 'classic' Automatic Speech Recognition (ASR). • In the SSPNet Conflict Corpus (SC 2 ), conflict occurs as a disruptive event that frequently results in speech overlaps, thus creating problems for ASR and speech modelling. • In the Geneva Multimodal Emotion Portrayals (GEMEP), pronounced but unrealistic portrayals of frequent and less frequent emotions, serves as a upper baseline for modelling a many-class problem and demonstrates the difficulty of this task even in 'ideal' conditions; it has been shown that, when going over to realistic, spontaneous data, performance considerably deteriorates  (Batliner et al. , 2000; Vogt and André, 2005). • In the Child Pathological Speech Database (CPSD), a-typical speech, which often forms a obstacle for standard ASR, can used for modelling these specific types of speech pathologies  (Bone et al.
Conclusions and Future Challenges In this work, we reviewed the first of its kind Computational Paralinguistics Challenge, which has been initialised to overcome comparability issues regarding data sets, partitioning, evaluation measures, baseline systems, and test-beds. The introduction of the common ComParE feature set, designed to tackle various paralinguistic recognition tasks, has proven very successful, as can be seen from the fact that most of successful participants' submissions employed the feature set or parts of it, and at the same time it has contributed to utmost comparability of results. Along with SVM, the ComParE features introduced here yielded competitive performance in the participants' field of the Conflict, the Emotion, and the Autism Sub-Challenge; yet, no single feature from the ComParE set was competitive on its own.
Acknowledgement The Marcello Mortillaro is senior scientist and Head of Applied Research at University of Geneva -Swiss Center for Affective Sciences -one the largest centers in the world entirely devoted to the study of emotion and other affective phenomena. He authored several publications in scientific international journals and has been awarded several grants from public (Swiss National Science Foundation) and private institutions (among others Wrigley Inc. ) to investigate the role of emotions in product development and service optimisation, as well as for development of new instruments to assess emotions and emotion-related skills.
research work has received funding from the European Community's Seventh Framework Programme [ASC-Inclusion, grant No. 289021], the Eu-ropean Union's Framework Programme for Research and Innovation HORI-ZON 2020 [ARIA-VALUSPA, grant No. 645378], and the European Union's Seventh Framework Programme ERC Starting Grant [iHEARu, grant No. 338164]. This research has been also supported by the Laboratory of Excellence SMART (ANR-11-LABX-65) supported by French State funds managed by the ANR within the Investissements d'Avenir programme (ANR-11-IDEX-0004-02). The authors would further like to thank the sponsors of the Challenge, the Association for the Advancement of Affective Computing (former HUMAINE Association) and the Social Signal Processing Network (SSPNet). The responsibility lies with the authors.
Results The Monte-Carlo simulations have been carried out using the Python library PyCATSHOO. (The flow functions φ m x were computed using a Runge-Kutta method of order 4 with a discretization step of 0. 01. This discretization step is small enough so that reducing it further does not change the estimations. ) As the Cross-Entropy method was not yet implemented in PyCATSHOO, we have used a specific Python code for the Cross-Entropy and the importance sampling methods. The system parameters used in the simulation were the following ones: x min = 0. 5, x max = 5. 5, x e = -1. 5, β 1 = 0. 1, β 2 = 5, t f = 100. Trajectories were all initiated in the state z 0 = 7. 5, (OF F, OF F, OF F ). The probability of having a system failure before t f was estimated to p = 1. 29 × 10 -5 with an intensive Monte-Carlo estimation based on 10 8 runs.
Objective: The main objective of this study was to measure the impact of a learning program via WhatsApp on clinical reasoning in medical residents. Methods: This prospective, randomized, multicenter study was conducted among first-and second-year anesthesiology residents (offline recruitment) from four university hospitals in France. Residents were randomized in two groups of online teaching (WhatsApp and control). The WhatsApp group benefited from daily delivery of teaching documents on the WhatsApp app and a weekly clinical case supervised by a senior physician. In the control group, residents had access to the same documents via a traditional computer electronic learning (e-learning) platform. Medical reasoning was self-assessed online by a script concordance test (SCT; primary parameter), and medical knowledge was assessed using multiple-choice questions (MCQs).
Both of these observational studies showed satisfaction among WhatsApp participants and highlighted the ease of use and the quick access to lessons through the app. However, there is no prospective study comparing WhatsApp to any other teaching modality. Residents involved in tutored practice exchange groups have better medical reasoning, as evaluated by the script concordance test (SCT), which is a well-validated medical reasoning-assessment tool for residents  [12] [13] [14]. Similar to practice exchange groups, WhatsApp allows direct communication between teachers and students with the possibility of discussing real clinical cases and commenting on residents' management of the case. Thus, we hypothesized that WhatsApp could have the same effect as practice exchange groups on clinical reasoning.
Agreement to participate was provided online by email or telephone by each resident who could stop participating at any time. The inclusion criteria were ongoing medical residency, possession of a mobile phone that could download the WhatsApp app, and attestation for agreement to use WhatsApp for this study. Noninclusion criteria were refusal to participate, noncompatibility of a mobile phone to download WhatsApp, and failure to download WhatsApp. This study was carried out in addition to the official teaching program of the residents and was not integrated into usual teaching nor did it replace previous teaching. The primary measure was medical reasoning evaluated by the SCT. The secondary parameters were medical knowledge measured by multiple-choice questions (MCQ); feasibility and acceptability of using WhatsApp, assessed by collecting resident testimonials; the Cronbach coefficient alpha, calculated after optimizing the test for SCT  [18] ; and self-assessment (by quantitative and semiquantitative numerical scales using a satisfaction questionnaire) of time spent working, quality of teaching, global satisfaction, teachers' availability, impression that the teaching met the learning objectives, relevance of clinical cases, and volume of teaching documents used.
The total duration of teaching was 3 weeks, and the choice of the length of the teaching period was based on both the availability of teachers and the estimated acceptability of students. In the control group, residents had access to the same documents via a computer e-learning platform, and the senior anesthesiologist teacher was available by email. They had access to the three clinical cases with their answers but had no live interaction with a teacher. The two groups had the same program and learning objectives. Participants did not receive any documents during the weekends and were free to stop the courses at any time. The characteristics of WhatsApp-assisted m-learning and traditional e-learning used in this study are summarized in Table  1. At the end of the teaching period, the two groups had the same formative evaluation by 29 SCTs and 30 MCQs sent by email and completed during the month following the end of the teaching period (Multimedia Appendices 4 and 5).
Residents who did not respond to the final evaluation were excluded from the final analysis (lack of analyzable parameters). After performing a Shapiro-Wilk normality test, the quantitative variables were compared using a Student t test (if the distribution was normal) or a Mann-Whitney test (if the distribution was not normal). The qualitative variables were analyzed using a Fischer or a chi-square test. The significance threshold was set at. 05. All statistics were analyzed using GraphPad PRISM software (v 5. 0; GraphPad Software Inc, San Diego, CA).
Results of the Script Concordance Tests and Multiple-Choice Questions The lessons took place from March 12 to 30, 2018. For final evaluation, SCTs including 12 scenarios for a total of 36 items were submitted to a panel of 13 experts. Thereafter, 7 items of the SCT were excluded (not enough variability in replies), leaving 29 items of SCT spread over 12 clinical situations. According to the recommendations of Lubarsky et al, we optimized SCT by performing a post-hoc analysis  [18]. Items with high variability, low variability, or binomial responses were excluded. We obtained a final version with 10 scenarios and 24 items. After this optimization, Cronbach coefficient alpha was. 55. In the WhatsApp group, 20 residents answered the preliminary evaluation, 1 resident who responded to the preliminary evaluation did not answer the final evaluation, and 3 residents who did not respond to the preliminary evaluation answered the final evaluation.
However, it is interesting to note that the comments from WhatsApp residents were very positive, with a higher overall satisfaction rating. The novelty and originality of the concept probably contributed to this satisfaction, but it underlines the fact that the students were not aware of the possible negative impact of the use of WhatsApp. A recent randomized pedagogic study assessed the impact of learning modules using m-learning on knowledge gain, skill gain, and satisfaction for otorhinolaryngology and head and neck surgery disorders in undergraduate medical students  [28]. Despite the absence of differences in knowledge gain in the mobile interactive multimedia group, satisfaction was higher in the mobile group (like in our cohort). Therefore, we can assume that our data are concordant with the literature on m-learning.
Monitoring vegetation phenology is critical for quantifying climate change impacts on ecosystems. We present an extensive dataset of 1783 site-years of phenological data derived from PhenoCam network imagery from 393 digital cameras, situated from tropics to tundra across a wide range of plant functional types, biomes, and climates. Most cameras are located in North America. Every half hour, cameras upload images to the PhenoCam server. Images are displayed in near-real time and provisional data products, including timeseries of the Green Chromatic Coordinate (Gcc), are made publicly available through the project web page (https://phenocam. sr. unh. edu/webcam/gallery/). Processing is conducted separately for each plant functional type in the camera field of view. The PhenoCam Dataset v2. 0, described here, has been fully processed and curated, including outlier detection and expert inspection, to ensure high quality data.
Background & Summary Phenology is broadly defined as the timing of recurring of biological events  1. Vegetation phenology exerts significant control over seasonal changes in ecosystem structure and function  2, 3. Key examples of this influence include the role of vegetation phenology in dictating the timing and magnitude of ecosystem carbon uptake  4, 5  , as well as seasonal shifts in energy and water fluxes between the surface and the atmosphere  [6] [7] [8]. Vegetation phenology is also sensitive to climate variability. In temperate and boreal forest ecosystems, phenology is predominantly driven by air temperature  [9] [10] [11]  , while in warm, arid grassland ecosystems, phenology responds strongly to the timing and magnitude of precipitation events  12, 13. Standardized and publicly-available phenology datasets can provide a key source of information to aid scientists and land managers in documenting-and anticipating-the impacts of climate change on terrestrial ecosystems  14, 15.