dataesr/scientific-paragraphs-categorization huggingface dataset:
We then treat IP as a hierarchical joint optimization problem, where first one of the possible single IP strategies (1-3) is chosen and then the number of attributes is decided, as shown in Fig. 5. At each generation step, the MDP can choose 1-5 attributes (e. g. cuisine, price range, location, food quality, and/or service quality). Generation stops as soon as the user is predicted to select an item, i. e. the IP task is successful. (Note that the same constraint is operational for the WoZ baseline. ) States are represented as sets of NLG dialogue context features, see Fig. 5. The state space comprises "lower-level" features about the realizer behavior, including two features representing the number of attributes (# attr) and sentences generated so far (# sentence), and three binary features representing the user's predicted next action, as well as "high-level" features provided by the DM, including the current database hits brought to the user's attention (# dbHits), the slots filled so far (statusSlot) and a subset of user actions (also see Section V-A).
C. Learned Behavior Note that these strategies are context-dependent: the learner chooses how to proceed dependent on the features in the state space at each generation step. In the following we provide illustrative examples of what was learned for each of the generation scenarios, which we extracted from the respective test runs. a) Scenario 1. 1-1. 2: IP Strategy Selection: The RL policy for Scenario 1. 1 learned to start with a SUMMARY if the initial number of items returned from the database is high ( ). It will then stop generating if the user is predicted to select an item. Otherwise, it continues with a RECOMMEND. If the number of database items is low, it will start with a COMPARE and then continue with a RECOMMEND, unless the user selects an item. In addition, the RL policy for Scenario 1.
2 learns to adapt to a more complex scenario: the number of attributes requested by the DM and produced by the stochastic sentence realizer. It learns to generate the whole sequence ( ) if #attributes is low ( ), when the overall generated utterance (final #sentences) is still relatively short. Otherwise the policy is similar to the one for Scenario 1. 1. b) Scenario 2. 1-2. 4: IP : The RL policies for jointly optimizing IP strategy and attribute selection learn to select the number of attributes according to the generation Scenarios 2. 1-2. 4. For example, the RL policy learned for Scenario 2. 1 generates a RECOMMEND with 5 attributes if the number of database hits is low ( ). Otherwise, it will start with a SUMMARY using 2 attributes. If the user is predicted to narrow down his focus after the SUMMARY, the policy continues with a COMPARE using 1 attribute only, otherwise it helps the user by presenting 4 attributes.
It has only one IP strategy, i. e. , the system only provides information about database entries in the form of single venue recommendations (the recommend strategy, see Table  I ). The attributes of the venue to be presented are selected heuristically. This baseline represents the information presentation strategies in conventional slot-filling systems, e. g. [4] ,  [32]. In the extended version of the system, the IP strategy is replaced by our trained NLG component, which is optimized to decide between different IP strategies. We follow a hybrid between statistical and rule-based approaches in order to integrate our trained IP policy: higher-level hand-coded rules impose a set of constraints on the statistical policy. Note that the possibility of constraining statistical policies with hard-coded rules is increasingly required for developing commercial dialogue systems  [65].
For the AMT corpus, considerably more subjects were used, although many of them did only a small number of tasks. For the LOC corpus, it was more difficult to recruit many subjects, but in this setup, the subjects could be asked to complete a minimum number of tasks, hence the higher average number of dialogues per user. Also, note that the word error rate (WER) is relatively high in both corpora. This is partly due to the fact that the ASR module had not been trained specifically for this particular domain due to lack of training data. Furthermore, some of the subjects were non-native speakers and some subjects used Skype to call the systems, which causes distortion of the audio signal. These conditions are the same for both BASE and TIP systems. Despite the high ASR error rates, overall task completion rates were relatively high  [67].
This work was supported by the  European Community 's  FP7  programmes: ( FP7/2007-13 ) under grant agreement no. 216594  ( CLASSiC ); ( FP7/2011-14 ) under grant agreement no. 270019  ( SPACEBOOK ); ( FP7/2011-16 ) under grant agreement no. 269427  (STAC); and supported in part by the  Engineering and Physical Sciences Research Council, U. K. , (EPSRC)  under project no. EP/G069840/1. The associate editor coordinating the review of this manuscript and approving it for publication was Dr.
Note that our approach is scalable to larger database sizes. The number of retrieved database items currently serves as a feature in the state space as well as a reward feature, where presenting large sub-sets of database items is negatively rewarded (see Section V-C). By increasing the number of items in the database, the size of the state space therefore increases, and learning would take more iterations. This feature could be quantized, or automatic state-space compression techniques could be used to maintain tractability [48].
Study design NIAMI, funded by the UK Medical Research Council, was a multicentre, double-blind, randomized, placebo-controlled trial designed to study the effect of sodium nitrite administered intravenously over 5 min immediately prior to reperfusion by PPCI on infarct size in patients presenting with first acute STEMI. The detailed study protocol has been published. 23 egulatory approvals were obtained from Scotland A Research Ethics Committee and the Medicines and Healthcare products Regulatory Agency (EudraCT number 2010-023571-26). The study was registered (NCT01388504, ISRCTN57596739) and overseen by trial steering and data monitoring committees. 23.
In 17 patients, sampling for plasma nitrite was undertaken prior to infusion and 5 min after completion of the infusion of study medication and for blood methaemoglobin prior to 10 min and 2 h after commencing study medication. Plasma nitrite was measured by high-pressure liquid ion chromatography with post-column derivatization using a dedicated analysis system (ENO-20 with Gilson 234 autoinjector, EPC-500 data processor and PowerChrome software; Eicom). Plasma was obtained by centrifugation of EDTA blood within minutes of collection, snap-frozen in liquid nitrogen and stored at 2808C. Frozen plasma samples were thawed in the presence of N-ethylmaleimide (10 mM final concentration) and deproteinized by methanol precipitation immediately prior to analysis.
Sample size The sample size was estimated using simulation methods. 23 Based on data from Botker et al. 7  and from our CMR database of patients studied 1 week following acute STEMI, we assumed the mean infarct size in the placebo group would be 15 -20%, and that a relevant treatment effect would reduce this by 4 absolute percentage points in the active group. We assumed that the mean AAR was 30% (SD 15) and the correlation between AAR and infarct size was 0. 6. Simulations showed that 150 participants (75 in each group) were required to detect the anticipated effect size with 90% power at the 5% alpha level. In previous published studies of conditioning interventions in acute STEMI, data loss from randomized patients were high. 7 Sample size was therefore inflated by 30 -35% to account for loss of primary outcome data due to death, contraindication to or failure to proceed to CMR, or inadequate CMR quality.
Primary outcome Primary outcome data were obtained from 85 of 118 (72%) participants in the nitrite and 88 of 111 (79%) in the placebo group. The reasons for not obtaining primary outcome data were: participants declining CMR [26 (22%) and 18 (16%) in the nitrite and placebo groups, respectively] and unreadable scans (12 in total). The median infarct size was 22% in the nitrite and 20% in the placebo groups [difference 20. 7% (95% CI: 22. 2, +0. 7; P ¼ 0. 30)] (Table  2 ). Multiple imputation sensitivity analyses gave similar results to the primary analysis, the treatment effect estimate from the comprehensive model was 20. 8% (95% CI: 22. 3, 0. 8; P ¼ 0. 34). Results were robust to missing-not-at-random assumptions for all but implausible scenarios, for example, delta 25% in the nitrite group but zero in the placebo group.
Secondary outcomes Biomarker data for sufficient datapoints to create a 72-h AUC were available in 158 patients (81 nitrite, 77 placebo). Reasons for incomplete biomarker data included participants discharged before 72 h and patients declining to have (usually nocturnal) samples taken. Patients with more than two missing datapoints were excluded. The AUCs for both biomarkers were similar in the two treatment groups (see Table  2  and Figure  2 ). The effect size was 2125 arbitrary units (95% CI: 21139, +888 units; P ¼ 0. 81) for Troponin I, and +5766 arbitrary units (95% CI: 28695, +20 288 units; P ¼ 0. 79) for CK. These results were consistent under all sensitivity analyses, except implausible scenarios. There was no significant difference in infarct size (5-SD technique) using AAR (T2) as a covariate between the nitrite and placebo groups (effect size +0.
Safety data Fifty serious adverse events (SAEs) were recorded in the safety dataset (280 participants where injection was started). There were 21 events in 19 of 146 participants (13%) in the nitrite group, 29 events in 25 of 134 participants in the (19%) placebo group. There was one death in the nitrite group and four in the placebo group: none was related to the study intervention. There were no suspected unexpected serious adverse reactions. Six SAEs were recorded as 'possibly related' to the study intervention but were listed in the protocol as expected events. Two were in the nitrite group and four in the placebo group.
DATA DESCRIPTION The CLEF 2013 eHealth Task 3 data comprises a document collection, a set of training and test topics, and relevance assessments. The official data has been released for non-commercial use. Document Collection. The document collection contains around one million documents, i. e. web pages from medical sites. The documents are predominantly health and medicine websites that have been certified by the Health on the Net (HON) Foundation 3  , as well as commonly used health and medicine websites such as DrugBank, Diagnosia, and Trip Answers. The documents are provided in the dataset in their raw HTML format along with their uniform resource locators (URLs). Topics. The topics (extended queries) were manually created by medical experts, based on information contained in hospital discharge reports.
The topic set comprises 5 training topics and 50 test topics. The queries in the collection aim to model those used by laypeople (i. e. patients, their relatives, or other representatives) to find out more about their condition, after they have examined their hospital discharge summary. The discharge summaries used for the task originate from the anonymized clinical free-text notes of the MIMIC II database, version 2. 5  4. Disorders have been identified within discharge summaries and linked to the matching UMLS (Unified Medical Language System) concepts generated in CLEF 2013 eHealth Task 1  [7]. Registered nurses and clinical documentation researchers developed a set of patient queries using the pairs of discharge summary and a disorder (randomly selected among all disorders identified) in order to generate a set of realistic patient queries.
Based on the main disagreement, the annotators discussed and reviewed the guidelines. After a second step annotation, they achieved 98% agreement. Specifically, they agreed on the number of concepts for all but one query. The main reason for disagreement was the definition of concept. While it seems rather straightforward to distinguish specific medical entities from general ones, some topics were ambiguous. For example, for the query "White blood cell and bacteria", the annotators could not reach any agreement: two annotators considered "bacteria" to be a concept while the third one did not. This query has been removed from the dataset for the analysis described in this paper. The topic distribution for the 50 test queries is as follows: 22 queries contain one concept (1-concept); 22 queries contain two concepts (2-concept); 5 queries contain three concepts (3-concept); and 1 query is ambiguous.
Also, s, p and d Rydberg functions,  80  each with effective quantum numbers n = 3, 3. 5, 4, were placed in the molecular centre of gravity. Six Cartesian components of the d functions were used in the calculations. The total number of molecular orbitals for basis B was 149. In the ADC(2) computations, the carbon K-shell atomic orbitals, as well as the sulphur K-and L-shell atomic orbitals, were kept frozen. The original polarization propagator ADC software  91  linked to the GAMESS-US package  92  was used in these calculations. In order to enable strict comparisons with the EOM-CCSD results, the ADC(2) calculations were performed also for basis A. Since these computations were beyond the capabilities of our ADC software,  91  they were carried out using the ADC(2) implementation in the MOLPRO program.
7σ. The observed B-mode power spectrum is well fit by a lensed-ΛCDM + tensor theoretical model with tensor-to-scalar ratio r = 0. 20 +0. 07 -0. 05 , with r = 0 disfavored at 7. 0σ. Accounting for the contribution of foreground dust will shift this value downward by an amount which will be better constrained with upcoming datasets.
C. Detector readout and data acquisition system The TES detectors were read out through time-division SQUID multiplexing chips provided by NIST. A single readout channel was connected in rapid succession to 32 detectors, reducing wiring and heat load requirements. These SQUID systems were biased and read out by a multi channel electronics (MCE) crate external to the cryostat (provided by UBC). The sample rate stored to disk was 20 Hz. The housekeeping and readout electronics were connected to a set of Linux-based computers running a control system called GCP, which has been used by many recent ground-based CMB experiments  [68].
III. OBSERVATIONS AND LOW-LEVEL DATA REDUCTION A. Observations BICEP2 observed on a three day schedule locked to sidereal time. As in BICEP1, the basic unit of observation was a ≈ 50 minute "scan set" during which the telescope scanned back-and-forth 53 times at 2. 8 • s -1 in azimuth in a smooth turnaround triangle wave pattern, with a throw of ≈ 60 • , at fixed elevation. We refer to each of the 106 motions across the field (either left-or rightgoing) as a "half scan". We do not use the turnaround portions of the scans in this analysis. BICEP2 observed the same CMB field as BICEP1-a low foreground region centered at RA 0h, Dec. -57. 5 •. At the South Pole, the elevation angle is simply the negative of declination and azimuth maps to RA shifting by 15 • per hour. The scan speed on the sky was thus ≈ 1.
See the Instrument Paper for more details of the observation strategy. The control system ran CMB observation schedules relentlessly between early 2010 and late 2012 collecting over 17 000 scansets of data (≈ 590 days). (There were some breaks for beam mapping and other calibrations during the austral summers. ) The raw data were transferred off site daily via satellite, allowing rigorous quality monitoring and ongoing analysis development. The analysis presented in this paper uses all of the CMB data taken by BICEP2.
B. Analysis pipeline The analysis pipeline used in this paper is written in the matlab language and was originally developed for the QUAD experiment  [46]. It was then adapted to BI-CEP1 data and became the secondary, and then primary, analysis pipeline for the C10 and B14 papers, respectively. For BICEP2 it has seen substantial further development including the addition of a sophisticated automatic data selection framework, full deprojection of beam systematics, and a map-based B-mode purification operation; these enhancements are detailed below.
D. Relative gain calibration At the beginning and end of each scan set an elevation nod or "elnod" was performed. The telescope was moved up-down-up or vice versa in a roughly sinusoidal excursion in time, injecting a signal proportional to the atmospheric opacity gradient into the detector time streams. In analysis, each elnod is regressed against the air-mass profile through which it was looking to derive a relative gain coefficient in SQUID feedback units per air mass. The time stream for each scan set is then divided by its own elnod coefficient and multiplied by the median over all good detectors. This roughly equalizes the gain of each channel and results in considerable cancellation of atmospheric fluctuations when taking the difference of detector pairs, thus making the data considerably easier to work with.
D. Binning into pair maps At this point we bin the pair-sum and pair-difference signals into per-scan set, per-pair RA-Dec. pixel grids which we refer to as "pair maps. " The pixels are 0. 25 • square at declination -57. 5 •. The data from each scan set are weighted by their inverse variance over the complete scan set (with separate weights for pair sum and pair difference). We note that while the pair-sum weights vary widely due to variation in atmospheric 1/f noise, the pair-difference weights are extremely stable over timei. e. , atmospheric fluctuations are empirically shown to be highly unpolarized. For pair difference a number of products of the time stream and the sine and cosine of the polarization angle are recorded to allow construction of Q and U maps as described in Sec. IV G below.
E. Second round data cuts The per-scan set, per-pair maps are recorded on disk to allow rapid recalculation of the coadded map while varying the so-called "second round" cut parameters. These include a variety of cuts on the bracketing elnods, including goodness of fit to the atmospheric cosecant model and stability in both absolute and pair-relative senses. We also make some cuts based on the behavior of the data themselves, including tests for skewness and noise stationarity. Many of these cuts identify periods of exceptionally bad weather and are redundant with one another. We also apply "channel cuts" to remove a small fraction of pairs-principally those with anomalous measured differential beam shapes. In general BICEP2 data are very well behaved and the final fraction of data retained is 63%.
This matrix is singular if a given pixel has been observed at only a single value of the deck angle modulo 90 •. In general for BICEP2 data we have angles 68  We perform absolute calibration by taking the cross spectrum of the T map with either the Planck 143 GHz map or the WMAP9 W -band T map as described in the Instrument Paper. We adopt an absolute gain value intermediate to these two measurements and assign calibration uncertainty of 1. 3% in the map to account for the difference.
ACKNOWLEDGMENTS BICEP2 was supported by the  U. S. National Science Foundation  under Grants No. ANT-0742818  and  ANT-1044978  ( Caltech and Harvard ) and  ANT-0742592  and  ANT-1110087  (Chicago and Minnesota). The development of antenna-coupled detector technology was supported by the  JPL Research and Technology Development Fund  and Grants No. 06-ARPA206-0040  and  10-SAT10-0017  from the  NASA APRA  and SAT programs. The development and testing of focal planes were supported by the  Gordon and Betty Moore Foundation at Caltech. Readout electronics were supported by a  Canada Foundation for Innovation  grant to UBC. The receiver development was supported in part by a grant from the  W. M. Keck Foundation. The computations in this paper were run on the Odyssey cluster supported by the  FAS Science Division Research Computing Group at Harvard University.
Soil texture and bulk density were analyzed from samples taken along several 2. 5 m deep profiles at different dates over the period to calibrate soil moisture sensors for volumetric water content. Consistent particle size distributions of ∼ 84-92 % sand and ∼ 5-13 % clay were found in all profiles. Porosity was estimated from bulk density, in the range of 0. 32-0. 36 m 3 m -3. For the practical and theoretical reasons mentioned earlier (e. g. , equipment failure, temporarily unsuitable conditions), the data series include gaps of variable lengths (10-35 % missing data). Meteorological variables, needed for model forcing, were gap-filled by substituting the closest available data from similar instruments deployed over the Wankama catchment  (Cappelaere et al. , 2009). Eddy covariance data were processed into half-hourly turbulent fluxes, using EdiRe software (R.
, 2011 Sjöstrom et al. , , 2013;; Lohou et al. , 2014). A field survey of vegetation phenology was conducted at both plots every 1 or 2 weeks from June through December of all 7 years  (Boulain et al. , 2009a). Of particular interest for this study are the seasonal courses of vegetation height and leaf area index (LAI). Height was sampled from 15-30 individuals per plot and date. LAI was derived from hemispherical photographs, following the protocol prescribed by the VALERI project (http://www. avignon. inra. fr/ valeri). They were acquired at 13 locations in a 20 × 20 m square in each plot, using a Canon EOS 500 numerical camera with a Sigma-8mm-F4 fisheye lens, and were processed with the Can-Eye software  (Weiss et al. , 2004). To obtain continuous daily series over the study period (Fig.
Acknowledgements. The first author's Ph. D. was financed by a student research grant from SIBAGHE Doctoral School at University Montpellier 2 (http://www. sibaghe. univ-montp2. fr). This work was made possible by data from the AMMA-CATCH observing system (http://www. amma-catch. org) in Niger, which is supported by  IRD ,  INSU , and OREME and is a component of the RBV research catchments network (http://rnbv. ipgp. fr). The study also benefited from the partial financial support of  ANR  ( ESCAPE  project) and  CNES  (TOSCA programme). Fruitful discussions with  A. Boone ,  V. Guinot ,  P. Hiernaux ,  M. Ibrahim ,  L. Kergoat ,  C. Leauthaud , and  C. Leduc  are gratefully acknowledged. The authors also wish to thank  IRD 's local representation in Niger (http://www. niger. ird. fr) as well as the residents of Wankama.
The proposed nomenclature is actually in line with ATC as it includes a therapeutics component (Axis 4 and 5) and a  The change-and the challenge-posed by the new nomenclature is that of turning the current nomenclature upside down; the indications driving the existing nomenclature dropping to Axis 5 and pharmacology being placed at the top of the list. While the five-axis system is convoluted and unwieldy, routine use might be based primarily on Axis 1 (the class). Moving to other axes is optional-dependent to a large extent on how far the clinician wishes to pursue the specific clinical situation facing him. However, the choice of psychotropic drug ideally should encompass the data presented in all axes yet with careful references to Axis 5 (approved indication).