dataesr/scientific-paragraphs-categorization huggingface dataset:
Results First of all, the parser tends to be conservative when predicting discontinuities, there are in general few false positives. The 72. 0 discontinuous F1 (Table  4 ) indeed decomposes in a precision of 78. 4 and a recall of 66. 6. This does not seem to be a property of our parser, as other authors also report systematically higher precisions than recalls  (Maier, 2015; Stanojević and Garrido Alhama, 2017). Instead, the scarcity of discontinuities in the data might be a determining factor: only 20% of sentences in the Discontinuous Penn Treebank contain at least one discontinuity and 30% of sentences in the Negra and Tiger corpus. Analysis results are presented in Table  10. For wh-extractions, there are two main causes of errors. The first one is an ambiguity on the extraction site. For example, in the relative clause which many clients didn't know about, instead of predicting a discontinuous PP, where which is the complement of about, the parser attached which as a complement of know.
Acknowledgments We thank  Kilian Evang  and  Laura Kallmeyer  for providing us with the discontinuous Penn Treebank. We thank  Caio Corro ,  Sacha Beniamine , TACL reviewers and action editor  Stephen Clark  for feedback that helped improve the paper. Our implementation makes use of the Eigen C++ library (Guennebaud and Jacob, 2010), treetools 13 and discodop. 14MC and SC gratefully acknowledge the support of  Huawei Technologies.
3. Independent of the type of information used, modelling approaches rely on a set of variables that condense and summarize observations. These strongly depend on source datasets, making their identification a critical step to operationalize the models and create the foundation for the design and implementation of monitoring systems. Within these, conservation managers are able to identify concrete groups of locally important variables that together describe the social-ecological system. 4. Observations constitute the source datasets that are the basic building blocks of any given monitoring system and of which the variables are derived. These are usually dynamic measures that require the consideration of both in situ and remote (e. g. satellite data) monitoring activities  (Vihervaara et al.
For instance, collection, mobilisation, and publishing of data regarding species distribution and population structure can use the standards, methods and tools being developed for essential biodiversity variables  (Kissling et al. , 2018). In doing so, automated data flows can be established and feed the development of global datasets critical for biodiversity monitoring and research  (Navarro et al. , 2018). Still, there are consistency and scalability issues when several conservation areas consider the same essential variable. As an example, different conservation areas can identify species distribution as a monitoring variable without the necessary thematic consistency (e. g. in Fig. 2a  species distribution corresponds to forest species while in Fig. 2b  it corresponds to cetaceans). Addressing such thematic, and eventually temporal, inconsistencies will be critical when considering interoperability across conservation areas.
Acknowledgement This work was supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  No 641762dECOPOTENTIAL project ("Improving future ecosystem benefits through earth observations "). The work of  Linwood Pendleton  and  Evangelia Drakou  was supported by the  "Laboratoire d'Excellence" LabexMER  ( ANR-10-LABX-19 ) at the  European Institute of Marine Sciences (IUEM).
The task was implemented using E-Prime v. 2. 0. 8 Professional (Psychology Software Tools, Pittsburgh, PA, USA) and presented using the Nordic Neurolab Solution (Nordic Neurolab, Bergen, Norway). The task was split into two runs of 5 min each. Each run consisted of 150 trials and four 10-second rest periods. A trial was the consecutive presentation of a smiley (duration of 200 ms) and a cross fixation point (duration of 1300 ms). A response was allowed until the next trial began, giving a constant inter-stimulus interval (ISI) of 1500 ms. Smileys appeared in a pseudo-random order: trials at the beginning of runs or following resting periods had to be Go cues. Go cues had to occur at least three times in a row and No-Go cues had to occur individually or at most two times in a row.
Acknowledgements We are grateful to the gynecologists, obstetricians, ultra-sonographers, midwives, pediatricians, and families who participated in the study. We particularly thank  Catherine Ducassoux  and the MR technicians for neuropsychological and MRI testing. This study is supported by the  Fondation de France (FdF)  and  Rennes University Hospital. The PELAGIE cohort is funded by  Inserm , the  French Ministries of Health, Labour, and Ecology, InVS ,  ANR , and.
Eventually multivariate analysis and feature ranking are performed. The best performing features are then combined into a predictive model. The performances of the resulting model is then assessed on an external validation dataset (Figures  1, 2 ). Due to the high number of interdependant features generated, ML algorithms are well-suited to render best performing models. The rise of research in radiomics has relied on the recent accelerated development of ML techniques. The reproducibility of such a model is also highly dependent of the imaging technique  (7, 8). Movement may alter the model performances as well as the time lapse between contrast injection and acquisition, or data processing from acquisition to display (from RAW images to DICOM images), known to vary across manufacturers. Several institutions have dedicated teams for radiomics research with an open source approach.
For instance, the radiomics. io plateform (http://www. radiomics. io/) was developed by Harvard University in order to develop and maintain open source projects, provide go-to resource for radiomic applications and promote radiomics within the science community. It provides links to major radiomics original articles and features extraction tools such as PyRadiomics  (9). The CASMI (Chinese Academy of Science, Medical Imaging) laboratory in Beijing has developed a website on which tools such as 3DMed (feature extraction software) and Radiomics Software (automated segmentation and radiomics classifier software) are available for download (http://www. radiomics. net. cn). It also launched a cancer data sharing plateform. Other features extraction softwares have also been developed and made available to the scientific community such as IBEX  (10) , LifeX  (11).
Machine Learning ML technique is a branch of artificial intelligence in which an algorithm learns by inference from a data set  (12). Its main objective is to produce a model capable of classification, prediction, and estimation of a situation from selected known data. As a result it may improve our decision-making process as it can encompass a higher number of parameters than humans  (13). Parameters coming from clinical observations, biology, genomics and radiomics data may improve the clinician decision-making process (Figure  3 ). The training phase is the process of finding dependencies in a system for a given dataset. Then, these dependencies are used to predict new outputs. ML methods are categorized into supervised and unsupervised learning. Supervised algorithms use labeled data (to map the data to the desired output classification) while unsupervised learning algorithms do not use labeled examples and provide a classification based on the patterns found in the dataset.
Semi supervised learning methods combine supervised and unsupervised learning when dataset combines both labeled and unlabeled data. The performance of a model is estimated on a validation sample, through sensitivity, specificity, accuracy and area under the curve (AUC) on the testing sample (Figure  4 ). If there is only one dataset, validation methods such as Holdout Method, Random Sampling, Cross-Validation, and Bootstrapping can be used  (14). Machine Learning methods include, but are not limited to: • ANN (artificial neural network) can be used to classify patients. Its main disadvantage is its high level of abstraction which makes it difficult to determine what features were used by the neural network. This disadvantage is called the "black box" effect. • DTs (Decisional Trees) renders a decisional tree to classify each input.
However, for a given question such as prognosis, many signatures with different features are generated resulting in a lack of agreement on the features to take into account  (17) (18) (19) (20). The ideal classification model should fit the training set and correctly classify each item in the test dataset. However, if misclassification error rate is low in the training set, and gets higher in the testing set, the model is overfitting and is not generalizable. As a result, validation on external cohorts is critical to prove its generalizability. Many models (not necessarily developed with machine learning techniques) are shared on plateforms such as http://www. predictcancer. org. This open source initiative may ease external validation of models by other teams on other populations and promoted by radiomics websites previously introduced.
The algorithm classified data that included ROIs and several dose metrics on a weekly basis. Eventually, the SVM model rendered a graph and a summary of the output. Unsupervised learning identified four categories: -Predictive: the data points are within the defined threshold. Re-planning is not needed; -Adaptive: Points are outside the defined threshold. The patient needs a replanification; -Errors: points that are outside thresholds because of a software bias; -Warning: points that are outside thresholds because of emaciation, organ motion, or incorrect set up; After a learning phase on 27 patients, the algorithm was tested on 13 patients. In the first 3 weeks, no replanification was needed for 84% of the patients. Then a reverse trend became significant at the beginning of the 4th week with 77% of replanification needed during the last 2 weeks of treatment.
As a result, the RFC model was preferred for prediction of severe mucositis for individual patient. It is available online at https:// github. com/jamiedean/oral-mucositis-model. The volume which received 220 cGy or more by fraction (V220) was the most important feature in both RFC models. The higher fractional dose volume (v240 and v260) were close to 0% and therefore did not correlate with mucositis. Age was the clinical covariate of highest importance but this may be artifactual due to the large number of possible values compared with the other clinical co variates. Besides, on univariate logistic regression, it was not significantly associated with severe mucositis. Another model was published (66) with a similar findings on dose response (volume of oral mucosa receiving more than 2 Gy per daily fraction was the most strongly associated feature).
). MeanBreadth and Spherical Disproportion were the most important features. MeanBreadth measures width of the ROI, thus it is correlated to tumor size. SphericalDisproportion represents the complication of the surface compared to a sphere. This may imply that HPV + tumors are smaller and less complex compared to HPV-tumors. The model performance was tested on two dataset coming from a public and a private hospital. The model performed an AUC of 0. 86667 on public leaderboard, and 0. 91549 on the private one which is a good indicator of generalizability. Clinical data such as grade or TNM stage although available, were not used as authors wanted to assess the image discriminatory power, and the discretization induced by these variables could add uncertainty to the final model.
Machine Learning on Clinical Factors for Prognosis Assessment Bryce et al. (  72 ) developed an artificial neural network on clinical factors and compared it to a logistic regression approach to create a predictive model of the 2-year recurrence free survival. They analyzed data from a phase III trial (73) that included patients with locally advanced head and neck squamous cell carcinoma (≥T3 or N3 or T2N0 base of tongue, hypopharyngeal, or pyriform sinus carcinomas) undergoing hyperfractionated accelerated radiation therapy with or without concurrent cisplatin and 5FU. Of the 116 patients of the study, 95 were included. Fourteen clinical variable potentially associated with 2 year PFS (Progression Free Survival) were available. Initial univariate analysis followed by stepwise multivariate analysis were first performed to select a subset of promising variables.
This signature was not correlated with HPV status and did preserve its performance in the HPV-group. Aerts et al. showed the predictive value of features selected for their stability and reproducibility which indicates the power of integrating independent datasets for feature selection and model building. Among those features, keeping only the top four performing features regarding prognosis to perform a single validation on 545 patients allowed a robust statistical analyse and avoided overfitting. The most dominant features of the signature quantified intra-tumor heterogeneity. Intra-tumoral heterogeneity reveals the presence of multiple colonies with different gene expression that cannot be encompassed by biopsies and therefore is an obstacle to personalized medicine and a potential prognosis marker.
One hundred and eighteen patients were included. Nine hundred and seventy features were extracted using Matlab. A radiomics signature from 8 features (5 from T1 weighted and 3 from T2 weighted images) yielded a C index of 0. 737 (95% CI: 0. 549-0. 924). This performance outperformed classic TNM staging system (C-index: 0. 514), but both combined yielded a better performance (C-index: 0. 761; 95% CI, 0. 664-0. 858) with a good calibration. Being able to predict not only the risk of recurrence but also whether it would be locoregional or a distant metastasis (DM) may enhance treatment decisions. As a result, Li et al. (82) explored the predictive power of radiomics for the type of recurrence (in field, out of field, marginal). After selection of the influential subset of radiomic features, Kruskal Wallis test and ROC analysis were employed for each feature to assess its capability on in field recurrence prediction.
After further selection, several machine learning models were trained (Artificial Neural Network, K Nearest Neighbor and Support Vector Machine). Three hundred and six patients were included with a median follow up of 26. 5 months. Eighteen in field, one marginal and one out of field recurrences occurred. Patients with in field recurrence could be differentiated by 8 features. The best performing model was artificial neural network (AUC: 0. 812). Zhang et al. (83) developed a MRI based model for distant metastasis (DM) prediction in nasopharyngeal carcinoma. DM is a common cause of treatment failure in nasopharyngeal carcinoma. One hundred and seventy-six patients from a retrospective multicentric cohort were included, 123 in the training cohort and 53 in the validation cohort. Pre-treatment MRIs, clinical and biological data were gathered.
The DM related feature with the maximum significance was "CET1-w_wavelet. HLL_ GLCM_Correlation. " It reflects the intra tumor heterogeneity from the gray scale extension perspective. The worse performance in the validation cohort suggested overfitting and further training on larger multicentric dataset should be considered. Still, being able to predict the risk of distant metastasis, or in field recurrence are promising prospective for radiomics support for clinical decisions. Kwan et al. (  84 ) performed a study with a similar goal on HPV related oropharyngeal carcinoma. Three hundred patients with HPV related oropharyngeal carcinoma were included. The 4 features radiomics features form Aerts et al. (  6 ) previously described study were used. The best performing model was a combination of the radiomic signature with clinical data.
Still, if all CT parameters were fixed except FOV, tube voltage, and milliamperage, then the information provided by radiomics could be summarized in only ten radiomics factors. On the other hand, too restrictive protocols may decrease the applicability to routine clinical care, all the more that with a given delay to acquisition, the actual enhancement varies depending on subjects. Another approach would be to gather CT scans with varying parameters to rule out noise in the dataset [as done by Aerts et al. (6) , presented previously]. The extracted features also depend on the extraction software, patient motion and image treatment. This may be one of the reasons for variability of the generated signatures between each study (Table  1 ). This creates a real challenge for its implementation in daily clinical care, the ultimate goal of radiomics.
2 and 1. 2s and with a pass energy of 23. 5eV. The peaks obtained are fitted by the Multipak software with a baseline and a series of peaks representing the chemical and oxidation state of the specific element. The curve-fitting is a Gaussianlorentzian function. To compare the effect of the laser and micro-knurling treatment on wetting, measurements of wettability were made with static contact angles using the sessile drop method  [32] [33] [34]. For the wetting measurements a goniometer (model DSA030) was used. 3µL droplets of distilled water were dispensed on the sample surfaces with a 0. 5mm needle from KRÜSS. For the reproducibility of results, 5 contact angle measurements were made at room conditions at 23°C and a relative humidity of 30-50%. The static contact angle mean average of these 5 measurements is then calculated and presented as the mean contact angle with a corresponding standard deviation.
Acknowledgements This work was supported by the  LABEX MANUTECH-SISE  ( ANR-10-LABX-0075 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR)  and by l' EQUIPEX MANUTECH-USD  ( ANR-10-EQPX-36-01 ). We would like to convey our gratitude to  Jules GALIPAUD  and  Thierry LE MOGNE  for their XPS expertise.
, 1963; King, 1996). For consecutive computational steps that use bispectra (i. e. , the bispectrum and the integration over the bispectrum), increasingly better constrained and noise-freer data is required, because each step zooms in further on these small amounts of variance. The bispectra of climate cycles presented here show clear interactions (Fig. 4 ), and the integrations of the bispectrum reveal qualitatively consistent patterns of energy transfers for analysis with varying window lengths (Fig. 5b  and Supp. Fig. 1 ). Therefore, we argue that the LR04 stack has sufficiently accurate age calibrations, and a high enough signal-to-noise ratio for the successful application of advanced bispectral analysis.
Resolvability in time and frequency domains Like the power spectrum, the bispectrum is marked by a trade-off between resolutions in the time versus frequency domain (Fig. S1 ). Bispectral results become more significant, and can yield greater degrees of freedom, if a larger number of similarly shaped cycles, or waveforms, can be included for analysis (i. e. , a lengthening of the window). For example, studies on natural nearshore waves or those on flume-generated waves (periodicities of seconds to minutes), use hour-long stable time series with high sampling resolutions and wave numbers  (de Bakker et al. , 2014). Bispectra analyses of such datasets are well resolved in the frequency domain. Furthermore, they permit the computation of robust confidence levels on the results, and the selection of statistical settings that yield high degrees of freedom (e.
4. 1. and Section 4. 2)  (Hasselmann, 1976; Bak et al. , 1987 Bak et al. , , 1988;; Bak, 1996; Pelletier, 1998; Huybers and Curry, 2006). Examples of interactions that could be studied in this way are those between astronomical and subastronomical (e. g. , millennial) climate cycles  (Hagelberg et al. , 1994; Wara et al. , 2000; Da Silva et al. , 2018) , and among cycles with centennial, decadal and annual durations  (King Hagelberg and Cole, 1995). We emphasize that other applications of bispectral analysis techniques to the palaeoclimate archive should be limited to records with good age control and high signal-to-noise ratios (see Section 4. 1. 1). Therefore, we argue that for these purposes data stacks are preferred.
Acknowledgments We thank  X. Bertin  for inviting D. L. to present this work at an early stage at the  Université de La Rochelle. We are grateful to  T. E. van Peer  for making us aware of the difficulties in reproducing geometries of nonergodic records. We thank  L. E. Lisiecki  and  M. E. Raymo , and  S. Ahn  et al. for making their benthic foraminiferal d 18 O stacks available online. We thank  T. Herbers  and  G. Ruessink  for providing bispectral analysis MATLAB scripts, which A. T. M. d. B. developed further for the purposes of this study. Clim. Past Discuss. , https://doi. org/10. 5194/cp-2019-43Manuscript under review for journal Clim. Past Discussion started: 24 April 2019 c Author(s)  2019. CC BY 4. 0 License.
Introduction The present book is in keeping with the aims of the Sustainable Urban Food Systems (SurFood) research project supported by Agropolis Fondation. This flagship project was set up to coordinate, amplify and globally showcase the interdisciplinary research of the different institutional members of Labex Agro. It has been written in response to a request from Agropolis Fondation's Science Council to build a conceptual framework for the analysis, evaluation and development of sustainable urban food systems.
Instruments are the procedural means that the departments or services have at their disposal (see chapter "Reconciling Sustainability Issues and Urban Policy Levers"). Regeneration, the first aim in Table  1 , uses food-related activities to revitalize the social fabric of an urban area that has become run-down following a crisis. In Detroit, urban agriculture could be a means of regenerating the city after its economic collapse, as Detroit is exempt from the State Right to Farm Act which otherwise prohibits agriculture in urban areas (Detroit Future City 2012). In Medellin, the crisis was sociopolitical, and funding was provided for urban agriculture projects by the State electricity provider as part of a long-running urban development programme  (Laidlaw 2015). In both of these examples, urban agriculture is central to regeneration efforts and can address problems of physical or economic access to healthy food, while boosting cognitive reconnection with food sources and food production, thereby helping to improve diets.
ACKNOWLEDGEMENTS We would like to thank our research partners the  Dolphin Discovery Centre  and the  City of Bunbury  for their support with this research. We also thank all of the visitors to Koombana Beach that participated in the survey that underpinned the research reported in this communication. This research was supported by the  Harry Butler Institute  and the  Murdoch University Vice Chancellor's Student Development Fund. We also thank the handling editor and the two anonymous reviewers whose insight, comments and suggestions improved this work. The study at the  Dolphin Discovery Centre  was conducted under  Murdoch University Human  Ethics Committee approval 2017/234.
H I G H L I G H T S • A large range of cubic structures was generated by using a continuum topology modelling, connectivites and properties of database can be directly plot in 2D color surface maps. • Evolution of elastic mechanical properties by the variation of relative density obtained by homogenization procedure was fitted by power law. • Power laws parameters represented the influence of topologies on the observed property. They were determined for cubic elastic constants and anisotropy. • Power laws parameters combined to computed connectivities led to the determination of different mechanical behavior families.