dataesr/scientific-paragraphs-categorization huggingface dataset:
As can be seen, C t equals SOC 0 at t = 0 and C S at t = ∞. The amount of C to be added to the soil each year in order to maintain SOC 0 , C a (Mg ha -1 yr -1 ), was calculated from the following equation, where "a" and "b" are the abovementioned coefficients of the linear regression equation between C input and SOC stock: ( ) 0 a SOC a C b - = Prior to analysis, data were checked for normality with the Kolmogorov-Smirnov test and homoscedasticity of variances with test. This was followed by analysis of variance (ANOVA) and, when differences between means were significant (p < 0. 05), by comparison with Tukey's test at p < 0. 05. The effects of the treatments on SOC were assessed using a MIXED procedure with treatment as the fixed factor and block as the random variable. All analyses were done with the software SAS ® v.
B. Apparatus All three experiments were conducted in a soundattenuated booth, and stimuli were presented through HD600 headphones (Sennheiser GmbH and Co. , Wedemark, Germany) via an AudioFire4 soundcard (Echo Digital Audio Corp, Santa Barbara, CA) connected to a DA10 D/A converter (Lavry Engineering, Poulsbo, WA) through Sony/ Philips Digital Interface. The output from this setup was calibrated to a level of 65 dB sound pressure level (SPL) (except for experiment 1, which was calibrated to 60 dB SPL) using a KEMAR head and torso assembly Type 45BA (G. R. A. S. Sound and Vibration, Holte, Denmark). All signal processing and stimulus presentations were performed in MATLAB R2014b (The Mathworks, Natick, MA) using a sampling frequency of 44. 1 kHz, and all data analyses were done in R (R Core Team, 2014).
Participants Fifteen NH listeners, aged 19-40 years old (l ¼ 25. 1 yr, r ¼ 5. 9 yr), participated in this experiment. Amongst the 15 participants, 12 had already taken part in similar experiments  (Gaudrain and Bas ¸kent, 2015). Their audiometric thresholds were tested at octave frequencies between 250 Hz and 8000 Hz and found to be all below 20 dB hearing level (HL). All participants had no history of hearing disorders, dyslexia, or attention deficit hyperactivity disorder, were generally in good health, and were either native Dutch speakers, or had Dutch as one of the languages used in their daily childhood environment. Participants provided signed informed consent prior to data collection, and the entire study protocol was approved by the ethics committee of the University Medical Center Groningen  (METc 2012.
ACKNOWLEDGMENTS The work presented here was jointly funded by  Advanced Bionics (AB) , the  University Medical Center Groningen (UMCG) , and the  PPP-subsidy of the Top consortia for Knowledge and Innovation of the Dutch Ministry of Economic Affairs. The authors have further been supported by a  Rosalind Franklin Fellowship  from the  University Medical Center Groningen, University of Groningen , and the  VICI  Grant No. 016. VICI. 170. 111from the  Netherlands Organization for Scientific Research (NWO)  and the  Netherlands Organization for Health Research and Development (ZonMw) , and funds from the  Heinsius Houbolt Foundation. This work was conducted in the framework of the  LabEx CeLyA ("Centre Lyonnais d'Acoustique ",  ANR-10-LABX-0060/ANR-11-IDEX-0007 ) operated by the  French National Research Agency , and is also part of the research program of the  Otorhinolaryngology Department of the University Medical Center Groningen: Healthy Aging and Communication.
Clinical data concerning age, gender, smoking history, chemotherapy, or EGFR Tyrosine Kinase Inhibitors (TKI) treatment, recurrence, and death were collected. A nonsmoker patient was defined as smoking less than 100 cigarettes in a lifetime. Pathological staging was based on the TNM (Tumor-Node-Metastasis) guidelines of the UICC (Union for International Cancer Control) 7th edition for the classification of lung cancer. 5 NSCLC were classified according to the 2004 WHO classification. 19 Adenocarcinomas (ADC) were re-classified according to recent IASLC/ATS/ ERS recommendations. 20 Follow-up was completed on 1 April 2015, which was defined as the deadline date.
Materials To ensure comparable and reproducible results, we apply the same dataset used in the two previous studies on multiplicative bases and morphosyntactic plural markers  (Her et al. , 2018; Tang et al. , 2018). The dataset comprises of a sample of 400 languages weighted according to geographical and genealogical factors. For instance, since the Austronesian family accounts for 17. 14% (1262/7363) of languages in the world  (Lewis, Simons, & Fennig, 2009) , the same ratio is applied in the dataset (19. 00%, 76/400). Likewise for geographical factors: Since the Pacific region accounts for 18. 74% (1380/7363) of the languages worldwide, a similar ratio is found in the dataset (18. 50%, 74/400). This dataset is not an absolute representative of all 7363 languages of the world, but it is estimated to be sufficient for macro-analyzes.
A visual representation of the 400 languages is shown in Figure  1. Each language in the dataset is annotated in terms of the features listed in Table  1. The features may be divided in the two main categories of grammatical information and metadata. Grammatical information relates to whether the language has morphosyntactic plural markers, multiplicative bases, and sortal classifiers. Metadata refers to the precise location of the language, along with its continent and genus affiliation. The last two features are included to assess the potential areal and genealogical effect on the distribution of sortal classifiers. Both genus and locations are extracted from the World Atlas of Language Structures, whereas the information of continent is based on Ethnologue. One major difference with previous studies is that we replaced the categorical variables of continent and genus by dummy variables.
The main motivation for such change is that information tends to be lost when a large amount of levels are considered as one variable. For instance, there are in total 234 genera attested for the 400 languages of the dataset; considering them as one variable is costly in terms of computational resource and would fail to capture the importance of every individual level within the categorical variable. For instance, the categorical variable continent is represented by five variables instead of one, c. f. , continent_Africa, continent_Americas, continent_Asia, continent_Europe, and continent_Pacific. Mandarin Chinese is located in Asia and thus has the value of 1 for continent_Asia and 0 for the four other dummy variables related to continent. As a general example, French is annotated as yes for morphosyntactic plural, yes for multiplicative bases, and no for sortal classifiers.
g. were classifier languages easier to identify than non-classifier languages. The two values of precision and recall are thus generated. Precision evaluates how many tokens are correct among all the output of the classifier, whereas recall quantifies how many tokens are correctly retrieved among all the expected correct output. The two measures assess different facets of the output, and are then combined into the f-score, which is equal to the harmonic mean of the precision and recall, i. e. 2(recall × precision)/(recall + precision)  (Ting, 2010). Finally, since the quantity of classifier and nonclassifier languages is unbalanced within the dataset, we use the rules of majority-label prediction (Zero rule) as a benchmark of accuracy. To be more precise, since more non-classifier languages than classifier languages are attested in the dataset (69.
Results The calculation of the current Section is realized via the packages rms, randomForest, randomForestExplainer, and party  (Harrell, 2015; Hothorn, Hornik, & Zeileis, 2006; Liaw & Wiener, 2002; Paluszynska, 2017)  from R (R-Core-Team, 2018). First, in order to clarify the complex interaction of the predictors evaluated by the random forests, we tested the statistical model of conditional inference tree with sortal classifiers as response variable and the parameters of numeral bases plus morphosyntactic plurals as explanatory variables. Then, we added the geographical and genealogical factors as explanatory variables to investigate their interactive and individual effect on the prediction of sortal classifiers in language. Finally, we extracted the importance of each variable from the random forests.
On the other hand, we observe a strong areal affect as within non-classifier languages, languages located in Asia are more likely to have plural markers, followed by the Americas and the Pacific. The ranking of variables according to their mean decrease of accuracy (Figure  8  plural markers, the variable of multiplicative bases only have a minor effect as it is only ranked 12th and 18th in both measures. Likewise in terms of mean minimal depth: the mean minimal depth of sortal classifiers is the smallest among all variables with 8. 93, whereas the mean minimal depth of multiplicative bases is 21. 61. However, we should point out that even though the statistical analyzes indicate that the correlation between morphosyntactic plurals and multiplicative bases is not as strong as theoretically expected, it may be worthwhile to further investigate the 20 languages in the current dataset that violate this correlation, most of which are minority languages in South America and some in Africa, to make sure that their indigenous numeral systems genuinely lack of multiplicative bases and thus that the absence of multiplicative bases is not due to language contact and the borrowing of multiplicative bases from the dominant language in contact, e.
g. Spanish in the South American context. Should some of these languages have indigenous multiplicative bases historically, a revised dataset might validate this correlation after all. To sum up, the output of random forests supports the first and the third hypothesis, and can be summarized as follow: if a language has sortal classifiers, it tends to have multiplicative bases and not to have morphosyntactic plural markers. However, the second hypothesis assumes the unification of sortal classifiers and morphosyntactic plural markers and suggests that if a language has morphosyntactic plural markers, it tends to have multiplicative bases. This hypothesis is not fully supported by our results, as the variable of multiplicative bases has very low predictive power with regard to the absence/existence of morphosyntactic plural markers in language when taking into account geographical and genealogical effects.
We extend the search for long-duration GW transients previously carried out on initial LIGO data from the period 2005-2010  [27]. Four pipelines have been used to double the frequency band coverage from (40-1000 Hz) to  (24-2048 Hz) , and new waveform models have been used to estimate the pipelines' sensitivities. We explicitly demonstrate that the search is capable of efficiently detecting three of the four potential sources mentioned above. No significant events were detected and consequently, upper limits have been set on the rate of longduration transient signals. The organization of the paper is as follows. In Section II, we describe the dataset. Section III is devoted to a brief description of the pipelines, whose sensitivities are presented in Section IV. In Section V, we give and discuss the results, then we conclude in Section VI with a discussion of future expectations.
II. DATA SET & DATA QUALITY This O1 analysis uses data from September 12, 2015 to January 19, 2016. The LIGO detectors in Hanford, WA and Livingston, LA ran with 40% coincident time. For this long-duration transient search, about two days of coincident data have been discarded because they were affected by major detector failures or problematic weather conditions. The remaining 49 days of coincident data still contain many non-stationary short duration noise events that can mimic a signal. These noise events, or "glitches", have a multitude of causes. For instance, low frequency glitches are caused by surges in power lines or seismic events, while many high frequency glitches are caused by resonances in the test mass suspension wires  [28]. Many of these effects can be tracked in auxiliary sensors that we use to define the severity of the loss of data quality  [28] [29] [30].
The signals targeted by the long-duration transients search have their energy spread over a large time span. Consequently, even modest excesses of noise directly influence the signal reconstruction. In order to be considered as a potential real signal, events must be seen coincidently in the two LIGO detectors. This requirement eliminates most of the noise events due to glitches. An accurate background estimation using the data themselves is therefore necessary to measure the significance of any coincident excess of energy. A false alarm rate (FAR) is estimated after safe veto methods are applied to get rid of as many glitch events as possible. While a few families of these noise events can be suppressed by vetoes based on auxiliary channels, each search pipeline has its own background reduction strategy and its own implementation of the time-slides method  [31]  to estimate the FAR.
Zebragard Working with tf -maps of size 24-2000 Hz × 500 s, Zebragard groups together pixels above a given SNR threshold that lie within a 4 pixel distance from each other. Because a sub-optimal number of sky positions are targeted, a signal can be anti-coherent (negative SNR). The algorithm addresses this in such a way that the loss of efficiency due to the limited number of tested sky positions is less than 10%  [33]. The trigger ranking statistic, Θ Γ , is defined as the quadratic sum of the SNR of the individual pixels. This analysis uses the same configuration and the same background rejection strategy against short-duration noise transient "glitches" (the fraction of SNR in each time bin must be smaller than 0. 5) as in  [27]. In addition, the O1 data set contains an excess of background triggers that required developing additional vetoes.
For example, using the fact that the two LIGO detectors are almost aligned, triggers due to a loud glitch in one detector are suppressed by demanding that the SNR ratio between the two detectors is smaller than 3. Mechanical resonances excited when the optical cavities of the interferometer arms are locked generate an excess of triggers at 39 Hz and 43 Hz at well identified times. Finally, the remaining glitches are efficiently suppressed by data quality vetoes based on auxiliary channels  [34]. It has been verified that these vetoes minimally affect the search for the targeted signals (less than 5% of simulated signals are lost). The background is estimated by time-shifting the data of one detector relative to the other in steps of 250 s. Data quality investigations and veto tuning are performed using a subset of the time-shifted triggers.
For example, the way the pipelines whiten the data or estimate the detector noise power spectrum may wash out continuous signals. This is the case for cWB and to a lesser extent for Zebragard and X-SphRad. Lonetrack by construction has no sensitivity to monochromatic signals and band limited white noise as these types of waveforms are not modelled by a Bezier curve. Figure  1  displays the GW energy emitted by a source located at 10 kpc for which the search efficiency drops below 50% for a FAR of 1 event in 50 years. The energy provides a universal quantity that can be directly compared to astrophysical predictions of the different possible sources. Assuming an isotropic GW emission, the energy emitted by a source at a distance r is given by E GW = c 3 r 2 4G ∞ -∞ ḣ2 + + ḣ2 × dt, (2) where ḣ+ and ḣ× are the time derivative of the GW strain for the two polarizations in the detector frame.
ACKNOWLEDGMENTS The authors gratefully acknowledge the support of the  United States National Science Foundation  ( NSF ) for the construction and operation of the  LIGO Laboratory  and  Advanced LIGO  as well as the  Science and Technology Facilities Council (STFC)  of the United Kingdom, the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/Germany  for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS)  and the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , for the construction and operation of the Virgo detector and the creation and support of the EGO consor-tium.
Cell Proliferation Assay Cells were seeded in 96-well flat-bottomed microplates and incubated for 24 h. The medium was then removed and replaced with fresh medium containing the RWE or RSV to be tested at increasing concentrations (from 1. 9 to 250 µg/mL) at 37 • C for 24 h. Each treatment was performed in sixplicate (in three independent experiments). The activity of compounds was determined using a solution of crystal violet (Sigma-Aldrich, St. Louis, MO, USA). Absorbance at 540 nm was measured by Biochrom Asys UVM 340. IC 50 (i. e. , the half maximum inhibitory concentration representing the concentration of a substance required for 50% in vitro inhibition) values were calculated using GraphPad 6. 0 Prism software (GraphPad Software, La Jolla, San Diego, CA, USA).
Confocal imaging was performed using a confocal laser-scanning microscope (Axio Imager M2, Zeiss) coupled with an Apotome. 2 with a ×40 objective lens, and Zen software (Carl Zeiss Microscopy GmbH, Germany). The samples were excited using internal microscope lasers and emission intensity was recorded at the appropriate emission wavelength. Image processing and analyses were carried out using ImageJ software. Negative controls were treated with the fluorochrome-conjugated secondary antibodies alone.
Statistical Analysis Results are shown as means ± SD for triplicate assay samples (otherwise mentioned), reproduced independently at least three times. Statistical analysis of data was carried out with Prism GraphPad 6. 0 Prism Software (GraphPad Software, Inc. La Jolla, San Diego, CA, USA). The significance of the differences between mean values was determined by a one-way ANOVA with Holm-Sidak correction. p-values < 0. 05 were considered significant (* p < 0. 05, ** p < 0. 01 and *** p < 0. 001).
Study design This study was conducted at nine French tertiary referral centers from April 2015 to March 2017. This was a prospective, randomized, controlled, open-label, single-blind, phase IV trial (Clinicaltrials. gov number NCT02559284) in patients receiving operations for steroid-resistant sinonasal polyposis. The study duration was 28 days and visits were scheduled at pre-inclusion, days 7, 14, 21, and 28. The trial was approved by both the French National Health Agency and the regional ethics committee, and all procedures performed were in accordance with the ethical standards of the institutional research committee and with the Helsinki declaration. Written informed consent was obtained from all participants included in the study.
Nasal irrigation method and education During the first 4 post-operative weeks, 4 nasal irrigations (240 mL/wash-120 mL/side) were performed daily with the mineral-rich solution or the saline solution. The investigators were not aware of the nature of the product that was allocated to the study subjects thanks to a randomization code and the products were delivered by pharmacies from investigating centers directly to the patient without any intermediary. Each solution was distributed within the post-operative cavities with the same transparent, squeezable and washable device (straight, watertight nozzle) (Netiflow® device, Laboratoire de la Mer® SAS, Saint-Malo, France). The nasal irrigation procedure required washing both nostrils with 180 mL of solution distributed equally on each side to remove crusting and clotting, a gentle blowing of the nose, and rinsing both nostrils with the remaining 60 mL of solution distributed equally on each side (no nose-blowing).
All tests were conducted using the p value approach, with a power of 20% and significance set at an alpha level of 0. 05. A univariate analysis was performed. Tests were conducted using SAS® software 9. 2 or later versions. Post-hoc analyses were performed on subgroups including subjects with most impaired RhinoQOL at pre-inclusion (frequency score ≤ 66, bothersomeness score ≤ 70, and impact score ≥ 32), patients over 60 years of age, and smokers  [23, 24].
We present the results from the fourth round of the WMT shared task on MT Automatic Post-Editing. The task consists in automatically correcting the output of a "black-box" machine translation system by learning from human corrections. Keeping the same general evaluation setting of the three previous rounds, this year we focused on one language pair (English-German) and on domainspecific data (Information Technology), with MT outputs produced by two different paradigms: phrase-based (PBSMT) and neural (NMT). Five teams submitted respectively 11 runs for the PBSMT subtask and 10 runs for the NMT subtask. In the former subtask, characterized by original translations of lower quality, top results achieved impressive improvements, up to -6. 24 TER and +9. 53 BLEU points over the baseline "do-nothing" system.
24 TER and +9. 53. On NMT data, the gains are less evident, with the largest improvements over the baseline of -0. 38 TER and +0. 8 BLEU. The large difference in terms of quality gains yield by APE can be explained in several ways. One is the different amount of in-domain training data available: in the PBSMT subtask, they comprise 28,000 instances while, in the NMT subtask, they are less than 14,000. 2 Another reason is the different MT output quality in the two datasets. Indeed, TER and BLEU scores for the PBSMT test set are respectively 24. 24 and 62. 99 while, in the NMT test set, they reach considerably better values of 16. 84 and 74. 73. Altogether, these differences contributed to make the NMT subtask more challenging, participants' scores concentrated in small TER/BLEU ranges close to the baseline and the overall results harder to interpret.
The first subtask, in continuity with the past, focused on handling translations produced by a domain-adapted phrase-based system (PB-SMT subtask). 3 The second subtask (NMT subtask) focused on handling translations produced by a domain-adapted neural system. 4 • The human post-edit (PE) is a manuallyrevised version of the target, which was produced by professional translators. Test data consists of (source, target) pairs having similar characteristics of those in the training set. Human post-edits of the test target instances are left apart to measure system performance. For the PBSMT subtask, the training data available include: i) all the 15,000 triplets (training, development and test) released for the 2016 round of the APE task and ii) the 13,000 training and test triplets released for the 2017 round, for a total of 28,000 instances.
Instances Table  1  provides basic statistics about the data, which was released by the European Project QT21  (Specia et al. , 2017). In addition, Table  2  provides a view of the data from a task difficulty standpoint. For each dataset released in the four rounds of the APE task, we report the repetition rate of SRC, TGT and PE elements, as well as the TER  (Snover et al. , 2006)  and the BLEU score  (Papineni et al. , 2002)  of the TGT elements (i. e. the original target translations). The repetition rate measures the repetitiveness inside a text by looking at the rate of non-singleton n-gram types (n=1. 4) and combining them us-ing the geometric mean. Larger values indicate a higher text repetitiveness and, as discussed in  (Bojar et al. , 2016; Bojar et al. , 2017) , suggest a higher chance of learning from the training set correction patterns that are applicable also to the test set.