dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental set-up 4. 1. The experimental context The data were acquired under various weather conditions from February to June 2017 at any time of day between 09. 00 and 17. 00. The experiments were performed near our laboratory in Marseille, France (43 14 0 01:16 00 N, 5 26 0 39:2 00 E). The experimental set-up is described in figure  2. The benchmark on which the present study was based consisted of rotating the celestial compass by 108 from 08 to 1708. At each rotating step, a full acquisition of the polarized skylight was performed with our celestial compass. According to the ESA (European Space Agency) and the TEMIS (Tropospheric Emission Monitoring Internet Service) UV program, the UV-index ranged from 1 to 8 (the UV-index is always given under clear sky conditions) 2. To compensate for the sun's course during the experiments, the ephemeris data corresponding to each experimental condition (day, time (UTCþ2 time) and location) were used to correct the AoP estimates.
g. +608 with respect to the zenith). A comparison with the angular range of the sky covered by the C. bicolor's DRA is made in figure  1   [16]. This angular FoV was adopted here in order to reduce the possible impact of visual occlusion by clouds or trees. The prototype was three-dimensional-printed using polylactic acid (PLA). The three-dimensional parts are available online 3. The celestial compass was controlled by a Raspberry Pi 2 model B microcomputer using Cþþ software. There was no need for any calibration processes before performing the measurements.
Our celestial compass, combined with the use of the AntBot method, can provide a useful alternative to GNSS devices in the case of the signal failures liable to occur in highly urbanized areas or near natural obstacles such as trees and canyons, for instance. Data accessibility. The datasets obtained in this study and the corre- sponding scripts are available online in the form of supplementary electronic material at: https://github. com/JuSquare/AntBot/tree/ master/HeadingPerformance.
Recruitment Eligible medical students will be those currently undertaking their rotational training in Psychiatry, which takes place in different years of training in different countries. An Information Sheet to eligible medical students will be distributed at least 24 h before the first session via email or by hand at a previous teaching session, and again at the start of the first session; written informed consent will then be sought at the start of the first session. One to one consent will be ensured through the availability of multiple members of the research team who will answer individual students' queries. Non-participating students will not be able to attend the training, as their receipt of the training without providing data may be perceived as unfair by participating students. It will be made clear to students prior to and at the start of the session that non-participation will not affect students' future performance on their medical course in any way.
Data management Data will be entered locally using a database provided by the research group at the lead site at King's College London. Pre and post data will be linked using a study ID number and no identifying information will be included. The cleaned, final dataset at each site will be returned to the lead at the King's College London site for analysis of the whole sample (both intervention and controlled arm). Data collection will be completed by the end of 2018. All data collection and data entry will be performed by trained research staff. If errors are found in the database, inconsistencies will be clarified by referring to the relevant hard copy. The quality of data collection will be supervised by senior researchers in the site, with overall supervision by the principal investigator.
A Microsoft Excel database has been developed to include data type checks and field guidance. Supervisors will monitor data quality, periodically carry out a set of pre-defined quality checks on the data, raise any queries with the relevant staff and identify missing data in the field. Where data are found to be missing, researchers will endeavour to revisit the participant to obtain the missing data. All storage of data will be in accordance with King's College London's data protection policy (http://www. kcl. ac. uk/college/policyzone/as sets/files/governance_and_legal/DataProtectionPolicy_ updated_Oct2011. pdf ). All data will be anonymised for analysis and will be accessible to the project investigators, the project co-ordinator and the data entry clerks within the INDIGO Partnership. Identifying data will be kept as a separate list with identifying codes, for the research records.
Statistical analysis Total scores for MAKS, MICA2 and the OSCE will be calculated so that a higher total score represents a more favourable outcome. Descriptive summaries of score changes will be calculated between the baseline and follow up time points for each group. Parametric and non-parametric tests will be employed to detect statistically significant differences in means on individual-level data before and after implementation of the intervention compared to the controls. Statistical analyses of individual-level data will be conducted using a random effects model accounting for clustering within sites and countries, to take into account the between country and culture variation in the attitudes towards mental illness. Changes in each outcome will be investigated at baseline and at the follow up time point after the intervention.
Process evaluation This study will follow guidance from the UK Medical Research Council on assessing the context, implementation and mechanisms of action. Site leads will be asked to provide relevant background information which may affect the implementation of the intervention, including: length of the Psychiatry rotation; year of degree course of in which the Psychiatry rotation takes place; whether OSCEs are routinely used in medical education and the psychiatry teaching curriculum. Students' attendance will be noted using sign in and sign out sheets for each session, asking for the time of the signature. The total number of students at each site and the proportion lost to follow up will also be recorded in the database of results. As part of fidelity assessment delivery of each component of the intervention will be monitored by a member of the research team at each site using a checklist.
Acknowledgements  Angela Mitchell  (SUITE -service user and carer and family focused part of the  Education and Training Department of South London and Maudsley NHS Foundation Trust). Lived Experience Network members,  Oxleas Foundation Trust. Open access for this article was funded by  King's College London. INDIGO  READ study group members. Graham Thornicroft  ( King's College London ),  Shuntaro Ando ,  Sosei Yamaguchi ,  Asami Matsunaga ,  Shinsuke Kondo ,  Kayo Ichihashi ,  Yasutaka Ojio ,  Makoto Ogawa ,  Chiyo Fujii ,  Kiyoto Kasai  ( The University of Tokyo Hospital, National Institute of Mental Health, National Centre of Neurology and Psychiatry, Japan ),  Andrea Candelas ,  Laura Martín ,  Andrea Jiménez ,  Cristina Castañeda ,  Cecilia Hernández  &  Jesús de la Higuera  ( University Hospital of Puerto Real, Cadiz, Andalusia, Spain ),  José Eduardo Muñoz-Negro ,  Mercedes Sola ,  Rocío García ,  José Miguel Gota ,  Juan Francisco Mula ,  Ana López ,  Amadeo Oria ,  Jorge A.
Such collections of intervals are called barcodes. We will consider a supersymmetric spectrum as a dataset, where each point is labelled by the charges or the relevant quantum numbers of a state, and by its degeneracy, or BPS enumerative invariant. We will then proceed to apply the methods of topological data analysis to compute the homology of this set as a function of a proximity parameter and compute its barcodes for each non-trivial homology group. The set of barcodes gives a complete characterization of the persistent homological features of the supersymmetric spectra. We will then discuss how these topological characteristics vary between different datasets. Roughly speaking we will do so in two ways: or by comparing spectra obtained within the same physical theory but as the parameters are varied; or by studying different models which can be however associated with a very similar physical problem.
All the computations in persistent homology have been done with the program matlab using the library javaplex, made available in  [64]. The accompanying software and datasets are available in  [14]. For the extraction of the BPS invariants and the manipulations of the relevant series we have used mathematica. This paper is organized as follows. Section 2 will give some background about BPS states, their wall-crossing behavior and their enumerative interpretation. In Section 3 we will give an elementary introduction to the ideas of persistence and discuss the methods of topological data analysis. This section is meant to be readable by non-experts and to quickly convey the main ideas. Section 4 contains our first application, to N = 2 SU(3) super Yang-Mills, where we compare the topological features of the BPS spectrum in two adjacent chambers.
Let ν ∈ N, and let d ν (x) be the distance between x and the ν-th closest landmark point (with d ν (x) ≡ 0 if ν = 0). Then for l 1 and l 2 points of L, [l 1 , l 2 ] is an edge in LW ν (X, L, ) if there exists a witness point x ∈ X so that the following condition holds max{d(l 1 , x), d(l 2 , x)} ≤ d ν (x) +. A higher dimensional simplex defines an element of LW ν (X, L, ) if and only if all of its edges are in LW ν (X, L, ) These definitions are those implemented in javaplex. It is easy to see that if 1 < 2 , then we have W(X, L, 1 ) ⊂ W(X, L, 2 ) and LW ν (X, L, 1 ) ⊂ LW ν (X, L, 2 ). We can now use the same arguments of Section 3 to argue that by letting vary we induce filtrations of witness and lazy witness complexes and that taking the i-th homology of any such sequence as a function of defines a N-persistence module.
On a more practical level, this note has taken to the task of exemplifying the uses of new methods based on persistent homology to study physical problems in string and field theory; as well as calling the attention of the computational topology community on the wealth of enumerative and number theoretical datasets which arise in string/field theories. From a physical perspective, to have a meaningful enumerative problem it is necessary to resort to protected quantities, in this case supersymmetric states. In the first part of the paper we have applied the tools of topological data analysis to the wallcrossing phenomenon in N = 2 theories. The indices of BPS protected quantities jump at walls of marginal stability and the effect of the jump can be in principle computed using a wall-crossing formula.
To fully understand how general is this result, and if one can turn it into a more quantitative statement, one should undertake a systematic study of the distributions of BPS states over all chambers in several models, both in field and in string theory. Practically this consists in starting from a chamber where the spectrum is known, if available, and then using repeatedly the wall-crossing formula until all the moduli space of vacua is covered. Repeating this for several theories will generate a large amount of data. Then a natural direction for this research program is to try to classify the typology of chambers by the topological features of the BPS distributions. Indeed we have seen examples in Section 4 and 6 where the topological features between different chambers differ drastically or are almost identical.
Acknowledgements I wish to thank the Applied Topology group at  Stanford University  for making javaplex available in [64]. I also thank the theory division at  CERN  and  IHES  for hospitality and support during the preparation of this note. This work was partially supported by  FCT/Portugal and IST-ID  through  UID/MAT/04459/2013 ,  EXCL/MAT-GEO/0222/2012  and the program Investigador FCT IF2014, under contract  IF/01426/2014/CP1214/CT0001. I am a member of INDAM-GNFM, I am supported by  INFN  via the  Iniziativa Specifica GAST  and by the  FRA2018  project " K-theoretic Enumerative Geometry in Mathematical Physics ".
Recent initiatives such as the PARSEME shared task have allowed the rapid development of MWE identification systems. Many of those are based on recent NLP advances, using neural sequence models that take continuous word representations as input. We study two related questions in neural verbal MWE identification: (a) the use of lemmas and/or surface forms as input features, and (b) the use of word-based or character-based embeddings to represent them. Our experiments on Basque, French, and Polish show that character-based representations yield systematically better results than word-based ones. In some cases, character-based representations of surface forms can be used as a proxy for lemmas, depending on the morphological complexity of the language.
Experimental Setup Corpora The PARSEME shared task 1. 1 released freely available VMWE-annotated corpora in 20 languages. 1 Each language's corpus is split into training, development and test parts. To choose our target languages, we analysed the PARSEME corpora, choosing 3 languages with varying morphological richness: Basque (EU), French (FR) and Polish (PL), shown in Table  1. 2 he FR training corpus has more than 420K tokens, whereas the PL and EU training corpora have around 220K and 117K tokens. EU contains less annotated VMWE occurrences than both FR and PL. The average length of annotated VMWE occurrences is similar in the three languages (2. 02/2. 29/2. 13 in EU/FR/PL). The proportion of discontinuous VMWEs in highest in FR (42. 12%), whereas in Polish (29. 76%) and in Basque (19.
6 We use the dev corpus as validation data, training for 25 epochs which 3 epochs of patience for early stopping. We configure it to use two layers of bidirectional gated recurrent units (GRU) of dimension 128, with all other parameters taking the default values suggested in the Veyn documentation. Word Representations We use two types of word embeddings to represent input surface forms 5 https://github. com/zamp13/Veyn 6 http://multiword. sourceforge. net/ sharedtaskresults2018 and lemmas: word2vec and FastText. Word2vec is a prediction-based distributional model in which a word representation is obtained from a neural network trying to predict a word from its context or vice-versa  (Mikolov et al. , 2013). Fast-Text is an adaptation which also takes into account character n-grams, being able to build vectors for OOVs from its character n-grams  (Bojanowski et al.
Conclusions and Future Work We have studied the impact of word representations on VMWE identification for Basque, French and Polish, comparing lemmas and surface forms as input features and comparing traditional word embeddings (word2vec) and subword representations (FastText). Regarding the latter, subword representations proved to be efficient for our task. For the former, we have highlighted that the use of lemmas always have a positive impact. For languages with high morphological richness, the combination of lemmas and forms has an even higher impact, especially for Basque. Considering the high Out-of-Vocabulary rate, including for verbs, we intend to improve OOV handling in the future. The use of recent embeddings such as BERT, Elmo and Flair, trained on large external corpora, could help with OOVs.
B. Data with ground truth measurements To evaluate the efficiency of our technique, we have used ground LAI measurements of 9 series of wheat and 9 series of rape acquired from January to July 2017 in Brittany, North France (5 ground measurements for rape and 4 ground measurements for wheat). These measurements act as ground truth validation. All series have been acquired on different parcels (whose size are larger than one SENTINEL-2 pixel) to sense the variability of our approach. Investigated areas are part of a Long Ecological Research site named "Pleine Fougres", located on the southern part of the Bay of Mont-Saint-Michel, France, and referenced in the LTER-Europe 1 and ILTER networks 2. This agricultural landscape is dominated by fields of wheat, maize, rapeseed and grasslands. Field observations and measurements were conducted on the study site during one crop year (2017) in order to calibrate and quantitatively validate the proposed approach.
C. Data with estimated LAI Data used are issued from real MODIS reflectance product (a sequence of 17 images) on which LAI estimations, issued from the PROSAIL model, have been reconstructed and validated (see  Lecerf et al. (2008 Lecerf et al. ( , 2005)) ). In addition, associated classifications of large agricultural parcels (we mean by large the fact that they recover more than one MODIS pixel) were available, providing us confident LAI time series and associated land cover. In practice three types of agricultural crops were used: colza, maize and wheat. Some LAI profiles are visible in Fig. 3. To model the various artefacts prone to occur in remote sensing images, several processes have been applied on these data: October 31, 2018 DRAFT • Noisy observations. Data are equally subsampled (one image every 10 days) from ground data and a Gaussian white noise has been introduced as shown in Fig.
Thus, the paradigm shift comes from the fact that the OpenAPS reference Besides this open-source initiative for diabetes technology, there are similar initiatives involving the data generated by diabetes medical devices. The most charismatic initiative so far is the Tidepool project  [31] , a non-profit open-source effort that has developed a hub for diabetes data, where patients with diabetes can gather and visualize, in one place, all their data from commonly used medical devices (insulin pumps, CGMs, blood glucose monitors, mobile devices) and also add their contextual information in real time to help them manage their disease. Moreover, the Tidepool platform uses a health crowdsourcing approach, encouraging patients to donate their data to create large databases for research purposes and Big Data analyses.
Artificial intelligence and diabetes AI has-and will continue to have-many applications in the field of diabetes. Already, AI has gained significant traction in recent years in the field of medical imaging  [32]. Automated diagnosis of diabetic retinopathy (DR) and cardiovascular risk factor monitoring are now possible, thanks to deep learning algorithms based on large retinal fundus imaging datasets  [33, 34]. Other AI algorithms will soon be integrated into smart telemedicine devices and be increasingly used to provide personalized preventative programmes, as well as personalized diabetes management adapted to patients' lifestyles, treatments, genetic backgrounds and environments  [35]. With constant improvement in models, closed-loop systems will also benefit from more powerful and accurate algorithms, thereby enabling a future artificial pancreas to take more and more parameters into consideration to predict the correct amount of insulin to administer  [36].
AI-based decisions will also be made by HCPs to choose the optimal line of treatment  [37] , based on deep or machine-learning algorithms trained on large medico-administrative or e-health record datasets  [38]. The use of AI will allow the processing, in real time, of massive datasets into actionable information for both patients and clinicians  [39] , including the implementation of large-scale interventions personalized as per patients' characteristics and adapted, over time, to prevent events such as hypoglycaemia and diabetes complications. Nevertheless, so far and surprisingly enough, and despite the increasing offer of digital solutions, relatively little scientific and independent evidence is available to guide patients, their HCPs or even researchers on how to make the best use of all such generated data to improve our understanding of diabetes and its complications.
Digital tools and sensors allow transition from the occasional assessment of disease to prospective real-time, continuous, high-throughput and no-burden remote monitoring of patients' symptoms, physiological data, behaviours, and social and environmental settings  [6, 7]. Furthermore, the causes of frequent hospitalizations related to chronic disease may be amenable to digital strategies that will eventually forego inpatient facilities  [40]. Impressive progress in data science techniques, natural language processing (such as learning from unstructured texts and speech recognition) and AI also enables the processing of heterogeneous and massive datasets collected by digital tools to help manage care, monitor patients and predict their outcomes  [41, 42]. Indeed, digital medicine could allow large-scale dissemination of interventions for remote and continuous behavioural changes at low cost and initially personalized, but adapted over the course of treatment on the basis of context, timing and previous responses  [6].
Accelerating the open-data movement in diabetes by liberating huge amounts of de-identified datasets is also necessary to ensure breakthroughs in diabetes research, although yet another fundamental requirement is that the validity of the data collected for research purposes should prevail over their 'bigness'  [71]. In addition, it is important to remember that the availability of new technology does not equate to acceptability by all. This means that ensuring adequate provision of evidence-based and informed education is now crucial, as is also co-designing services hand-in-hand with patients to maximize their utility. Similarly, there is a growing need for technical and psychological support of patients, which still needs to be addressed with the use of both conventional and new technologies.
Beverley Balkau  for her diligent proofreading of this review. Guy Fagherazzi  takes full responsibility for the work as a whole, and for the decision to submit and publish the manuscript. Guy Fagherazzi  is supported by the  French Research Agency (Agence Nationale de la Recherche ) by an  Investissement d'Avenir (Investment for the Future) grant  ( ANR-10-COHO-0006 ) that supports the E4N study and the  IDEX Paris Saclay Nutriperso  project.
2003). Tilts of the radio emission in all scales indicate that the jet is precessing and that it could impact the ISM at various locations. Indeed, an outflow of atomic gas had been seen in absorption by  Morganti et al. (2004 Morganti et al. ( , 2013a ) in clouds at the tip of the small-scale radio emission. The outflow had long been studied in its ionized phase  (Holt et al. 2008 (Holt et al. , 2009 (Holt et al. , 2011)). Dasyra & Combes (2011) ,  Guillard et al. (2012) , and  Dasyra & Combes (2012)  reported the outflow detection in rotational H 2 lines and in CO(2-3) absorption. The outflow had yet to be seen in CO emission. To further investigate the physical conditions of the lowtemperature molecular gas in the wind of 4C12. 50 (including its location, mass, and excitation), we acquired new millimeter interferometric data of CO (1-0), (3-2), and (4-3).
The ALMA data and their reduction The Atacama Large Millimeter Array (ALMA) observed 4C12. 50 for the cycle 2 program 2013. 1. 00180. S (PI Dasyra) in band 3 (July 2015), and in bands 7 and 8 (May 2015). At the time, the array had 37 operational antennas. In all bands, four spectral windows of 1. 875 GHz bandwidth were employed. In band 3, we used one spectral window to cover the velocity range -3300 km s -1 to 2100 km s -1 around the CO(1-0) line, and one spectral window to cover the velocity range -3800 km s -1 to 1100 km s -1 around the HCS + (3-2) line. The other two spectral windows targeted the continuum at rest frame 2. 641(±0. 026) mm and 2. 364(±0. 021) mm. In band 7, we used two partially overlapping spectral windows to cover the velocity range -2600 km s -1 to 900 km s -1 around the CO(3-2) line, and two spectral windows to obtain the restframe 896(±3) µm and 902(±3) µm continuum.