dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017; Yin et al. , 2017) , and 2) reinforcement learning to inject the task objectives within the machine translation model  (Nogueira and Cho, 2017). More particularly, we propose a two-step model which first learns the translation model through the supervision of NL-query pairs and then refines the translation model using a relevance feedback provided by the search engine. It is worth mentioning that there does not exist SOCS-oriented dataset that both aligns users' information needs in NL with keyword-based queries and includes a document collection to perform a retrieval task. To the best of our knowledge, TREC datasets are the only ones expressing such constraint, but the number of NL-query pairs is however limited. To fit with the issue of dealing with large vocabulary and the dataset constraint, we frame the translation model as a word selection one which aims at identifying which words in the NL expression can be used to build the query.
Datasets Since there does not exist yet SOCS-driven datasets including NL-query pairs, we use TREC tracks  (namely, Robust 2004 and Web 2000 -2001). In these tracks, query topics include a title, a topic description and a narrative text; the two latter being formulated in natural language. To build query-NL pairs, we use the title to form the set of keyword queries and the description for the set of information needs expressed in NL. An example of a query-NL pair is: This NL-query building process results in 350 pairs in total as presented in Table  1. We are aware that the use of TREC datasets is biased in the sense that it does not exactly fit with the expression of NL information need in the context of conversational systems, but we believe that the description is enough verbose to evaluate the impact of our query building model in this ex-   (Song et al.
, 2017) , will be carried out in the future. We also analyze the issue of duplicate words into TREC descriptions since it can directly impact the query formulation process based on word selection in the word sequence of TREC descriptions. In practice, this might lead to select several times the same word to build the query, and, therefore, directly impact the retrieval performance. As shown in Table  1 , the ratio of duplicate words in TREC descriptions over the whole set of queries is very low (1. 1 duplicate words in average in each query for TREC Robust and 0. 65 for TREC Web). This suggests that this issue is minor in the used datasets. We, therefore, decided to skip this issue for the moment.
Metrics and baselines To evaluate our approach, we measure the retrieval effectiveness of the predicted queries. To do so, for each predicted query, we run the BM25 model through an IR system (namely, PyLucene 1  ) to obtain a document ranking. The latter is evaluated through the MAP metric. To show the soundness of our approach (namely, translating information needs expressed in NL into queries), we compare our generated queries to scenario NL feeding the natural language information needs (TREC descriptions in our protocol) to the IR retrieval system. Since the objective of our model is to formulate queries, we also evaluate the effectiveness of original TREC titles (scenario Q). This setting rather refers to the oracle that our model must reach. We mentioned that before training the selection model we transformed each x to its binary representation y based on the presence of the words in the ground truth query.
The dataset being slightly biased by this binary modeling, we observed that not all the words existing in the query do exist in x. To analyze this bias, we also compare our approach with these binary queries (scenario Q bin) referring to the projection of queries Q on the vocabulary available in the NL description. We also compare our model to a random approach which randomly selects 3 words from x to build queries (scenario Random). Different variants of our model are also tested: • SMT which only considers the first component of our model based on a supervised machine translation approach (Section 2. 2). This variant could be assimilated to the approach proposed in  (Song et al. , 2017)  in the sense that the machine translation is performed independently of the task objective. • RL which only considers the reinforcement learning objective function (Section 2.
Results We present here the effectiveness of our approach aiming at generating queries from users' information needs expressed in NL. In Table  2 , we present the retrieval effectiveness (regarding the MAP) of our model and the different baselines (NL, Q, Q bin, Random, SMT, and RL) described in section 3. 2. From a general point of view, results highlight that in both datasets, our proposed model SMT+RL outperforms the different baselines with improvements that are generally significant, ranging from +3. 22% to +468. 91%. More particularly, the effectiveness analysis allows to draw the following statements: • The overall performance of the compared approaches generally outperforms the retrieval effectiveness of the NL baseline. For instance, on TREC Robust, queries generated by our model allows to significantly improve the retrieval performance of +15.
While queries in Q identify the most important words leading to an exploratory query (e. g. "steel productions"), our model SMT+RL provides additional words that precise which facet of the query is concerned (e. g. , "new methods of. "), and accordingly improves the ranking of documents. • Our model SMT+RL is significantly higher than the SMT baseline which converges to a relatively low MAP value (0. 06845 and 0. 08891 for TREC Robust and TREC Web, respectively). This could be explained by the fact that our datasets are very small (250 and 100 NL-query pairs respectively for TREC Robust and TREC Web) and that such machine translation approaches are wellknown to be data hungry. Reinforcement learning techniques could be a solution to overpass this problem since they inject additional information (namely, the reward) in the network learning.
3) which helps the model to be more general and effective before using the reward signal to guide the selection process. It is worth mentioning that we also trained in preliminary experiments a state of the art translation models such as a generative encoder-decoder RNN with attention mechanism, in  (Yin et al. , 2017; Song et al. , 2017). We did not report the results since the model was not able to generalize in the testing phase over new samples from the NL-query dataset used in the training phase. This is probably due to the trade-off between the number of training pairs and the large size of the vocabulary which is not enough represented in different contexts. However, we believe that combining reinforcement learning with attention-mechanism for query-generation is promising. We let this perspective for future work.
Conclusion and future work We propose a selection model to transform user's need in NL into a keyword query to increase the retrieval effectiveness in a SOCS context. Our model bridges two lines of work dealing with supervised machine translation and reinforcement learning. Our model has been evaluated using two different TREC datasets and outlines promising results in terms of effectiveness. Our approach has some limitations we plan to overcome in the future. First, our model is framed as a word selection process that could be turned into a generative model. Second, experiments are carried out on small datasets (250 and 100 NL-query pairs) that could be augmented using the evaluation protocol proposed in  (Song et al. , 2017). In long term, we plan to adapt our model by totally skipping the query formulation step and designing retrieval models dealing with NL expressions.
Study 2 In all, three cats in the control group were removed from trial after the baseline sample owing to behavioural unsuitability for sample collection. Data from these cats are not included in the analysis. In all, four cats were removed from the study before 28 weeks; one in the control group was removed in week 19 for behavioural reasons and its data were not included after this time. In all, three test group cats were removed after the 21-week sample point revealed changes in serum biochemistry (creatinine and urea outside of normal range) consistent with an adverse effect on renal function and their data were included up to and including week 21. For behavioural reasons, occasional missed   S5 ). No significant (P > 0•05) between-group differences in the change in energy intake were determined at any time (online Supplementary Table  S5 ).
Acknowledgements The authors acknowledge the skills and expertise of colleagues at WALTHAM in the care and training of the cats and for the analysis of samples collected throughout the study. This work was funded by  Mars Petcare. The  WALTHAM ® Centre for Pet Nutrition  is a fundamental research centre for  Mars Petcare. J. S. ,  J. Alexander , D. E. , M. G. , P. M. , J. E. , R. B. and P. W. conceived the project. J. S. ,  J. Alexander , D. E. , P. W. and M. G. developed the overall research plan and had study oversight. J. Alexander , J. S. , M. G. and  J. Atwal  conducted the research and oversaw the analysis of the samples. H. R. carried out and assessed the abdominal ultrasounds and radiographs. R. S. and A. C. performed the statistical analysis. J. Alexander  authored the paper with J.
Kirchheimer et al. 2016). Twenty-three of these populations were purely diploid, 60 purely tetraploid and 19, all from a narrow contact zone in France, contained individuals of both cytotypes. We coupled these presence data with field absences (i. e. confirmed true absences for the species R. kuepferi sensu lato, and hence also for both cytotypes) at 8239 non-forest plots extracted from the Alps vegetation database  (Lenoir et al. 2012). These plots were also distributed across the entire Alps and cover elevations between 1000 and 3400 m a. s. l. , that is, they bracket the elevational range of R. kuepferi. The combined data set was then used to parameterize four different algorithms: generalised linear models (GLM); generalised additive models (GAM); boosted regression trees (GBM); and random forests (RF).
Demographic modelling and pollinator competition The suitability of a site in a particular year was translated into the cytotype's local population dynamics by relating demographic rates as sigmoidal functions to SDM projections. Demographic rates were thereby constrained between zero and a maximum value extracted from field data (or, in some cases, estimated, Table  S1 ). In addition, germination and juvenile survival were modelled as density dependent, that is, their rates decline with the growth of the local adult population (Appendix S1). The annual seed yield of a population at a certain site (and year) was calculated as the product of the population size and two demographic rates: flowering frequency and seed number per individual. For the sexually reproducing, diploid and outcrossing cytotype, at least two adult individuals were required for seed set.
This index hence measures how precisely the simulations recapture the observed range size ratio of the two cytotypes. We subsequently used this metric as the response variable in a linear regression with niche, reproductive mode and their interaction as predictors. The variance explained by these factors was computed from their respective partial R 2 values. As a backup, we additionally calculated and inspected DAIC c values of the candidate models using the aictab function of the R-package 'AIC-modAVG' (version 2. 1-1, Mazerolle 2013).
ACKNOWLEDGMENTS BK and SD were supported by the  Austrian Science Fund  (project number: I 1189), EH, CCFS and SK by the  German Research Foundation  (project number  Ho 4395/1-1 ). JCS was supported by the  European Research Council  ( ERC-2012-StG-310886-HISTFUNC ) and also considers this work a contribution to his VILLUM Investigator project " Biodiversity Dynamics in a Changing World " funded by  VILLUM FON-DEN. We thank  Siegrun Ertl ,  Christian Gilli  and  Franz Hadacek  for help with fieldwork and two anonymous reviewers for their constructive comments, which helped us to improve the manuscript.
Methods A literature search was conducted utilizing online research databases: Medline, Global Health, Science Direct and PubMed. A combination of search terms was used to identify relevant articles. Search terms are summarized in Table  1. School or community based interventions that involved strategies to reduce consumption of high-sugar foods and beverages among children and adolescents were included. Studies were excluded if they were conducted outside of the Pacific Islands or targeted adults. Studies were also excluded if they didn't provide results of the intervention.
Exploring these determinants may have enabled researchers to target their strategies more effectively. Various factors are known to determine dietary intake in children and adolescents. This includes individual characteristics, social and physical environmental influences and societal influences. Firstly, individual characteristics, such as attitudes, beliefs and knowledge, influence food choice  [34, 45]. There is limited data available on the perception of high-sugar foods and beverages in children and adolescents in the Pacific Islands. Studies in this population worldwide suggest the perceived 'healthiness' of a product may influence consumption  [46]. This is important to note considering the way that many high-sugar products, such as sports drinks and energy drinks, are marketed. This age group often takes words and phrases commonly used in advertisements and on food packaging at face value (e.
The protocols performances are evaluated in terms of throughput, RTT, jitter and security parameters. In  [4] , authors addressed the performance evaluation of Secure Socket Tunneling Protocol (SSTP) and Internet Key Exchange Version 2 (IKEv2) VPN protocols. Based on a test-bed environment where VPN clients exchange data, it was found that IKEv2 is more efficient than SSTP in terms of throughput, jitter, and delay. In  [5] , a patent proposed Dynamic Group VPN (DGVPN) approach that used a unique key for data encryption and decryption of the same gateway during each session established between two gateways. The proposed model handles the principle of security group where a set of gateways may share the same security associations. However, it lacks scalability given that the same VPN router should be associated with several groups to profit from different security levels.
C. Quantitative risk evaluation The overwhelming priority for remote health monitoring is to make the right decision at the right time. For instance, any loss of data availability can degrade the patient's condition. In addition, confidentiality of the patient's medical records should be protected on the basis of the contextual information. Hence, security countermeasures should be applied while considering the risk level in order to ensure a tradeoff between security effectiveness and network performance. The aggregated risk is defined as: Aggregated_ Risk (s, o, a, c) = (w1• Context_ Risk) + (w2 • privileges_ Risk) + (w3 • history_ Risk) ( 3 ) Where Context_ Risk and privileges_ Risk are obtained by applying (  1 ) and (  2 ), respectively, history_ Risk is acquired from the database, and w1, w2, w3 are weights attributed to each category of metrics.
ACKNOWLEDGMENTS We are indebted to  R. C. Saunders  and  E. A. Murray  for performing the lesions. We thank  D. P. Soucy  and  J. Peck  for their technical assistance,  T. Minamimoto ,  A. Lerchner ,  J. M. Simmons ,  S. Ravel ,  M. Mishkin , and  R. C. Saunders  for useful discussions. This study was supported by the  Intramural Research Program  of the  National Institute of Mental Health  (project number  ZIAMH002032 ). During the completion of this work GLC was supported by  NIH  ARRA  P30 NS069256  start up funds. The views expressed in this article do not necessarily represent the views of the NIMH,  NIH , or the  United States Government.
Material and methods From January 1 st , 2001, to 30 June 2017, the hospital records of all women delivered at the maternity of the University South Reunion Island (ap. 4,300 births per year) were abstracted in a standardized fashion. All data were entered into an epidemiological perinatal data base which contained information on obstetrical risk factors, description of deliveries and neonatal outcomes. As participants in the French national health care system, all pregnant women in Reunion Island have their prenatal visits, biological and ultrasound examinations, and anthropological characteristics recorded in their maternity booklet. In our term pregnancies the average of prenatal visits was 9. 2 AE 2, and 4. 2 AE 1. 7 ultrasounds per pregnancy. In the general analysis, there were three criteria of exclusion: Preterm births (<37 weeks), multiple births and fetal deaths (in utero fatal deaths, stillbirths and medical termination of pregnancy).
Small for gestational age (SGA, 10 th percentile) and Large for gestational age babies (LGA, !90 th percentile) have been defined by a local curve established from our perinatal data base  [24]. Reunion Island is a French department in the Southern Indian Ocean. The peculiarity of this tropical region lays in the multiethnic origin of inhabitants [Africa and intermixed population (50%), Europe (27%), India (20%) and China (3%)]. Compared to Europe and mainland France, there is a younger reproductive population (the mean age at primiparity is 23 years). Finally, accessibility to maternity services is easy, and high-quality care is provided free of charge by the French healthcare system. Epidemiological data have been recorded and analysed with the software EPI-INFO 7. 1. 5 (2008, CDC Atlanta, OMS), EPIDATA 3.
Model The simulation is carried out for a period of 7 days during winter (from December 18 to December 24). This period has been chosen, because in winter, windows are usually opened less often, potentially leading to higher indoor pollutant concentration from internal sources  [15]. It is performed in Dymola (Modelica language) using the validated Modelica Buildings library  [35, 36]. A multi-zone approach with one zone per room is applied. The modelling methodology is detailed in a previous paper  [32]. The Dymola built-in Esdirk45a solver is used with a tolerance of 10 -6. Input parameters described in the following sections are pre-processing before being entered in the model (Fig. 1 ). Model outputs consist of the room CO 2 concentrations that are used to compute the indicators of indoor CO 2 concentration defined in "Indicators of indoor CO 2 concentration".
Constant parameters In this study, only the uncertainties that would have a possible impact on CO 2 concentration are considered. Average and standard deviation values for the design and physical parameters are presented in Table  2. They are chosen according to the literature  [2, 3, 6, 40, 41]. Truncated normal distributions bounded between ± 2σ are generated with the OpenTURNS software  [48]. The number of this type of parameters is equal to 25.
Acknowledgements This research was conducted under the scope of the  Agence Nationale de la Recherche (ANR)  funded  Maevia  project. The authors acknowledge the financial support provided by the  ANR  ( ANR-12-VBDU-0005 ). We would like to thank  Silvia Moreno Sans  for providing a proofreading of the manuscript. The authors are also grateful to the reviewers for their recommendations that contributed to improve this paper.
Calculation of physiological parameters All data are presented as the mean ± standard deviation (SD) of biological replicates (N ≥ 3). Lag phase was estimated using DMFIT (http://www. ifr. ac. uk/safet y/DMfit ), as described previously  [56]. The biomass yield was obtained as the slope of the linear curve when plotting the biomass concentration versus the glucose concentration during exponential growth on glucose. The specific rates of substrate consumption and product formation were calculated as described previously  [57]. The evaporation rate of ethanol was determined in a separate cell-free experiment, and all data were corrected for the evaporation of ethanol (1% of the ethanol at each point).
Plasmid construction OLE1 encoding ∆ (9) FA desaturase (GenBank Accession Number: NC_001139. 9) was amplified from genomic DNA of S. cerevisiae CEN. PK 113-7D using high-fidelity DNA polymerase (Thermo Fisher Scientific) with the primers OLEF (5′-CGC GGA TCC ATG CCA ACT TCT GGA ACT ACTAT-3′) and OLER (5′-CCC AAG CTT TTA AAA GAA CTT ACC AGT TTC GTA -3′). A 1533-bp PCR fragment including the entire coding region was obtained and then inserted into the 2-micron plasmid p426TEF  [62]  under the TEF promoter with BamHI/HindIII to yield the plasmid p426TEF-OLE1. The expression levels of ∆ (9) FA desaturase and squalene epoxidase (ERG1) were knocked down using the antisense oligonucleotide method, as described previously  [63]. The antisense oligonucleotide of the conserved catalytic domain of OLE1 was created by annealing the primer pair ELOF (5′-CCC AAG CTT TGG GGC CAC TCT CAC AGA ATT CAC C ATC GTT AC-3′)/ ELOR (5′-CGC GGA TCC GTA ACG ATG GTG AAT TCT GTG AGA GTG GCC CCA-3′).
Acknowledgements This work was financed by  Vinnova  Grant No. 2012-02597 ,  Biovacsafe  Grant No. FP7 ,  115308  (http://www. biovacsafe. eu/)and the  Novo Nordisk Foundation. We are grateful to  Suwanee Jansa-Ard  ( Systems and Synthetic Biology, Chalmers University of Technology ) for her help with lipid analysis. We would also like to thank  Michael Gossing  for his comments and constructive discussions.
Patient population and study design This was a post hoc analysis performed using data from the previously published AGREE study ( ClinicalTrials. gov identifier NCT00122382). 28 29 Briefly, MTX-naïve patients with early RA (≤2 years since diagnosis) who were positive for RF and/or ACPA antibodies and had evidence of erosion were randomised 1:1 to receive abatacept (~10 mg/kg intravenously according to weight range) plus MTX or placebo plus MTX (hereafter referred to as 'MTX alone') over a 12-month double-blind period followed by open-label abatacept plus MTX for an additional 12 months. 28 29 At baseline, all patients had high disease activity based on a tender joint count of ≥12, a swollen joint count of ≥10 and C-reactive protein (CRP) levels of ≥0. 45 mg/dL. determination of autoantibody titres Serum samples to assess levels of RF and ACPA (by assessment of second-generation anti-cyclic citrullinated peptide-2 (CCP-2) antibodies) were taken at screening and at months 6 and 12 of the double-blind period and stored at -80°C.
statistical analysis In the original study, DAS28 (CRP)-defined remission was evaluated for the intent-to-treat population, with patients who discontinued considered to be non-responders. For the purpose of this report, all analyses were descriptive and were based on patients with DAS28 (CRP) and CDAI data available at baseline and months 6 and 12. The proportions of patients achieving remission according to DAS28 (CRP) and CDAI were analysed as point estimates with 95% CIs, estimated using classic binomial distribution. Cumulative probability of time to achieve first remission and sustained first remission according to DAS28 (CRP)-defined and CDAI criteria were evaluated based on Kaplan-Meier estimates with 95% CIs. Patients who lost remission status were censored at the time of remission loss.