dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Methods Kaplan-Meier survival analyses were performed to compare the overall survival for galsulfase-treated versus ERT-naive patients. The difference in overall survival was compared using the log-rank and Wilcoxon tests. Any missing dates of death were imputed using the most conservative method, whereby the latest HR was estimated by using Cox proportional hazards model (both for univariate as well as multivariate analyses); HRs are presented with 95% CIs. Survival data from the pharmacovigilance database are provided as percentage surviving.
Survival-Pharmacovigilance The above findings are further supported by data from the pharmacovigilance monitoring of galsulfase. From the time of regulatory approval in 2005, through August 2016, 1193 patients with MPS VI have received 5 years of galsulfase ERT. Over this period, 149 deaths have been reported, corresponding to a 5-year mortality for treated patients of 12. 5%. Where data were available in the pharmacovigilance database, the age at death of these patients was 16. 0 (9. 9) years (n ¼ 116; mean [SD]), and the average time on treatment of the deceased patients was 3. 5 (2. 8) years (n ¼ 108). In 125 of the 149 patients, cause of death was identified and included respiratory dysfunction (30%), respiratory infection (22%), cardiac dysfunction (18%), cardiopulmonary dysfunction (13%), and infection (6%).
20 Additionally, a primary effect of galsulfase may be to improve pulmonary function. Harmatz et al have reported that FVC and FEV 1 improved over time in a combined cohort of patients with MPS VI from phases 1, 2, and 3 studies of galsulfase and that this improvement in pulmonary function occurred independent of age. 21 Although there was a significant difference in survival between the ERT and ERT-naive groups, we recognize that the small number for the ERT-naive group may diminish the general applicability of this observation. Results from the data collected in the pharmacovigilance database on the commercial use of galsulfase also support the finding of reduced mortality with treatment. This cohort represents the majority of the estimated MPS VI population globally. 9 Mortality in this group, which received an average of 5 years of galsulfase therapy, was 12.
Further study is warranted. Although only mortality data were assessed in the current study, several long-term studies demonstrate galsulfase safety and tolerability. 19] 22  Galsulfase treatment-related serious AEs are rare: only 31 events were reported for 11 patients in a 5-year follow-up of patients in a clinical surveillance program designed to follow up ERT patients (n ¼ 123). 22 Most patients developed immunoglobulin G antibodies to galsulfase,  13, 23  but only in 1 (1/38) patient could impairment of enzyme uptake or activity be substantiated. 13 Lin et al reported skin rash, dyspnea, and pyrexia in approximately one-third of their patients (n ¼ 9). 18, 19 These AEs were easily controlled with oral antihistamines, steroids, and antipyretics. No patient discontinued treatment due to AEs.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study and preparation of the manuscript were funded by  BioMarin Pharmaceutical, Inc. This publication was also supported in part by the  National Institutes of Health (NIH) National Center for Advancing Translational Sciences , through  UCSF-CTSI  grant number  UL1 TR000004  ( Dr. Harmatz ). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the  NIH. Giugliani  is the recipient of a  CNPq/Brazil  fellowship (Grant # 306091/2014-3 ). This manuscript was prepared according to the  International Society for Medical Publication Professionals' "Good Publication Practice  for Communicating Company-Sponsored Medical Research: The GPP3 Guidelines.
The training data consists of source-target parallel sentences and their corresponding images. • Task 1b: Multisource multimodal translation takes an image with a description in three source languages that is then translated into a target language. The training data consists of source-target parallel data and their corresponding images, but where the source sentences are presented in three different languages, all parallel. Task 1 is identical to previous editions of the shared task, however, it now includes an additional Czech target language. Therefore, participants can submit translations to any of the following languages: German, French and Czech. This extension means the Multi30K dataset  (Elliott et al. , 2016 ) is now 5-way aligned, with images described in English, which are translated into German, French and Czech.
1  Task 1b is similar to Task 1; the main difference is that multiple source languages can be used (simultaneously) and Czech is the only target language. We introduce two new evaluation sets that extend the existing Multi30K dataset: a set of 1071 English sentences and their corresponding images and translations for Task 1, and 1,000 translations for the 2017 test set into Czech for Task 1b. Another new feature of this year's shared task is the introduction of a new evaluation metric: Lexical Translation Accuracy (LTA), which measures the accuracy of a system at translating correctly a subset of ambiguous source language words. Participants could submit both constrained (shared task data only) and unconstrained (any data) systems for both tasks, with a limit of two systems per task variant and language pair per team.
Datasets The Multi30K dataset  (Elliott et al. , 2016)  is the primary resource for the shared task. It contains 31K images originally described in English  (Young et al. , 2014)  with two types of multilingual data: a collection of professionally translated German sentences, and a collection of independently crowdsourced German descriptions. Over the two last years, we have extended the Multi30K dataset with 2,071 new images and two additional languages for the translation task: French and Czech. Table  1  presents an overview of the new evaluation datasets. Figure  1  shows an example of an image with an aligned English-German-French-Czech description. This year we also released a new version of the evaluation datasets featuring a subset of sentences that contain ambiguous source language words, which may have different senses in the target language.
Multi30K Czech Translations This year the Multi30K dataset was extended with translations of the image descriptions into Czech. The translations were produced by 15 workers (university and high school students and teachers, all with a good command of English) at the cost of EUR 3,500. The translators used the same platform that was used to collect the French translations for the Multi30K dataset. The Czech translators had access to the source segment in English and the image only (no automatic translation into Czech was presented). The translated segments were automatically checked for mismatching punctuation, spelling errors (using aspell), inadequately short and long sentences, and non-standard charac-  ters. The segments containing errors were manually checked and fixed if needed. In total, 5,255 translated segments (16%) were corrected.
Test 2018 dataset As our new evaluation data for Task 1, we collected German, French and Czech translations for the test set used in the 2017 edition of the Multilingual Image Description Generation task, which only contained English descriptions. This test set contains images from five of the six Flickr groups used to create the original Flickr30K dataset 2. We     (Elliott et al. , 2017) ), as described above. The new evaluation data for Task 1b consists of Czech translations, which we collected following the procedure described above. Table  2  shows the distribution of images across the groups and tasks. We initially downloaded 2,000 images per Flickr group, which were then manually filtered by three of the authors. The filtering was done to remove (near) duplicate images, clearly watermarked images, and images with dubious content.
Dataset for LTA In this year's task we also evaluate systems using Lexical Translation Accuracy (LTA)  (Lala and Specia, 2018). LTA measures how accurately a system translates a subset of ambiguous words found in the Multi30K corpus. To measure this accuracy, we extract a subset of triplets form the Multi30K dataset in the form (i, aw, clt) where i is the index representing an instance in the test set, aw is an ambiguous word in English found in that instance i, and clt is the set of correct lexical translations of aw in the target language that conform to the context i. A word is said to be ambiguous in the source language if it has multiple translations (as given in the Multi30K corpus) with different meanings. We prepared the evaluation dataset following the procedure described in  Lala and Specia (2018) , with some additional steps.
The human annotator looked at both the image and its description and then selected the following subset {kappe, mütze, mützen} as the correct lexical translations clt that conform to the context of the test instance in Figure  2. We also asked annotators to expand the clt set with other synonyms outside the lt set that satisfy the context if they can. The number of ambiguous words and instances for each language pair in the resulting dataset for the test instances is given in Table  4. For English-Czech, while the first human filtering step (dictionary filtering) was not performed, the second human filtering step (test set filtering) was done. We note that this cleaning done by the Czech-English annotators was very selective, most likely due to the noisier nature of the initial annotations from the unfiltered dictionary.
CUNI (Task 1) The CUNI submissions use two architectures based on the self-attentive Transformer model  (Vaswani et al. , 2017). For German and Czech, a language model is used to extract pseudo-in-ID Participating team AFRL-OHIOSTATE Air Force Research Laboratory & Ohio State University  (Gwinnup et al. , 2018)  CUNI Univerzita Karlova v Praze  (Helcl et al. , 2018)  LIUMCVC Laboratoire d'Informatique de l'Université du Maine & Universitat Autonoma de Barcelona Computer Vision Center  (Caglayan et al. , 2018)  MeMAD Aalto University, Helsinki University & EURECOM  (Grönroos et al. , 2018)  OSU-BAIDU Oregon State University & Baidu Research  (Zheng et al. , 2018)  SHEF University of Sheffield  (Lala et al. , 2018)  UMONS Université de Mons  (Delbrouck and Dupont, 2018)   (Lin et al. , 2014).
The re-ranking cross-lingual WSD models are based on neural sequence learning models for WSD  (Raganato et al. , 2017; Yuan et al. , 2016)  trained on the Multimodal Lexical Translation Dataset  (Lala and Specia, 2018). More specifically, they train LSTMs as taggers to disambiguate/translate every word in the source sentence. For Task 1b, the SHEF team explores three approaches. The first approach takes the concatenation of the 10-best translation candidates of German-Czech, French-Czech and English-Czech neural MT systems and then re-ranks them using the same multimodal cross-lingual WSD model as in Task 1. The second approach explores consensus between the different 10-best lists. The best hypothesis is selected according to the number of times it appears in the different n-bests. The highest ranked hypothesis with the majority votes is selected.
Task 1: English → German Table  6  shows the results on the Test 2018 dataset with a German target language. The first observation is that the best-performing system, MeMAD 1 FLICKR DE MeMAD-OpenNMTmmod U, is substantially better than other systems, although it uses unconstrained data. The MeMAD team did not submit a constrained or monomodal submission, so we cannot conclude whether this improvement comes from the use of multimodal data or from the additional parallel data. However, as mentioned in Section 3, the authors themselves state that the gains mainly come from the additional parallel text data in the monomodal system. The vast majority of systems beat the strong text-only Baseline by a considerable margin. For other teams submitting monomodal and multimodal versions of their systems (e.
Task 1: English → French Table  7  shows the results for the Test 2018 dataset with French as target language. Once again, the MeMAD 1 FLICKR FR MeMAD-OpenNMTmmod U system performs significantly better than the other systems. 6 For teams submitting monomodal and multimodal versions of their systems (e. g. CUNI and LIUMCVC), there does not seem to be a marked difference in automatic metric scores. Another interesting observation is that in this case the clearly superior performance of the MeMAD 1 FLICKR FR MeMAD-OpenNMTmmod U system also shows in the LTA evaluation. All submissions significantly outperformed the English→French baseline system. For this language pair, the evaluation metrics are in better agreement about the ranking of the submissions, however, the LTA metric is once again less correlated.
Task 1: English → Czech The Czech language is a new addition to the 2018 evaluation campaign. Table  8  shows the results for the Test 2018 dataset with Czech as target language. A smaller number of teams have submitted systems for this language pair. This is a more complex language pair as demonstrated 6 We note that their original submission had tokenisation issues, which were fixed by the task organisers. by the lower automatic scores obtained by the systems. The best results are obtained by the CUNI 1 FLICKR CS NeuralMonkeyImagination U system, under the unconstrained conditions. The constrained systems all perform similarly to each other, and all except CUNI 1 FLICKR CS NeuralMonkeyTextual U are significantly better than the baseline system. Interestingly, for the OSU-BD submissions, LTA seems to disagree significantly with the other metrics.
Task 1b: Multisource English, German, French → Czech Multisource multimodal translation is a new task this year. This task invites participants to use multiple source language inputs, as well as the image, in order to generate Czech translations. Only a few systems have been submitted compared to the other tasks. The results for the Test 2018 dataset are presented in Table  9. We observe that all teams outperformed the text-only baseline, even though in some cases the difference is not significant. No teams used unconstrained data in their submissions. Again, the LTA results do not follow those of the automatic metrics, particularly for the two top submissions: LTA scores differ by a large margin, while all other metric scores are the same or very similar. This could however result from the very small number of samples available for LTA evaluation for this task: only 52 test instances.
Results For Task 1 English-German translation, we collected 3,422 DAs, resulting in a minimum of 300 and a maximum of 324 direct assessments per system submission, respectively. We collected 2,938 DAs for the English-French translations. This results in a minimum of 280 and a maximum of 307 direct assessments per system submission, respectively. We collected 8,096 DAs for the Task 1 English-Czech translation, representing a minimum of 1,330 and a maximum of 1,370 direct assessments per system submission. For Task 1b English,German,French→Czech translation, we collected 6,827 direct assessments. The least evaluated system received 1,345 assessments, while the most evaluated system received 1,386 direct assessments. Tables  10, 11 , 12 and 13 show the results of the human evaluation for the English to German, English to French and English to Czech Multimodal Translation task (Test 2018 dataset) as well as the Multisource Translation task.
Results of the human evaluation of the WMT18 English-German Multimodal Translation task (Test 2018 dataset). Systems are ordered by standardized mean DA scores (z) and clustered according to Wilcoxon signed-rank test at p-level p ≤ 0. 05. Systems within a cluster are considered tied, although systems within a cluster may be statistically significantly different from each other (see Table14). Systems using unconstrained data are identified with a gray background.
Results of the human evaluation of the WMT18 English-French Multimodal Translation task (Test 2018 dataset). Systems are ordered by standardized mean DA score (z) and clustered according to Wilcoxon signed-rank test at p-level p ≤ 0. 05. Systems within a cluster are considered tied, although systems within a cluster may be statistically significantly different from each other (see Table15). Systems using unconstrained data are identified with a gray background.
Results of the human evaluation of the WMT18 English-Czech Multimodal Translation task (Test 2018 dataset). Systems are ordered by standardized mean DA score (z) and clustered according to Wilcoxon signed-rank test at p-level p ≤ 0. 05. Systems within a cluster are considered tied, although systems within a cluster may be statistically significantly different from each other (see Table16). Systems using unconstrained data are identified with a gray background.
Results of the human evaluation of the WMT18 English,French,German-Czech Multisource Multimodal Translation task (Test 2018 dataset). Systems are ordered by standardized mean DA score (z) and clustered according to Wilcoxon signed-rank test at p-level p ≤ 0. 05. Systems within a cluster are considered tied, although systems within a cluster may be statistically significantly different from each other (see Table17).
Acknowledgements This work was supported by the  CHIST-ERA   M2CR  project ( French National Research Agency  No. ANR-15-CHR2-0006-01 -Loïc Barrault  and  Fethi Bougares ), by the  MultiMT  project ( EU H2020 ERC Starting Grant  No. 678017  - Lucia Specia  and  Chiraag Lala ), and by an  Amazon   Research Award (Desmond Elliott ). We thank the  Charles University team  for collecting and describing the Czech data. The Czech data collection was supported by the  Czech Science Foundation , grant number  P103/12/G084.
In contrast, the few evaluation studies against reference in situ observations suggest much better accuracy with little variability in the bias. More such studies are required for a better error characterization. N d uncertainty is dominated by errors in r e , and therefore, improvements in r e retrievals would greatly improve the quality of the N d retrievals. Recommendations are made for how this might be achieved. Some existing N d data sets are compared and discussed, and best practices for the use of N d data from current passive instruments (e. g. , filtering criteria) are recommended. Emerging alternative N d estimates are also considered. First, new ideas to use additional information from existing and upcoming spaceborne instruments are discussed, and second, approaches using high-quality ground-based observations are examined.
g. ,  Conant et al. , 2006; Snider et al. , 2003)  showing agreement of the parcel models with in situ observations to within 20%  (Fountoukis et al. , 2007). This paper critically reviews the current approaches for satellite N d estimates (section 2), highlights progress that has been made in addressing outstanding issues, assesses currently available data sets (section 3), and discusses some promising alternative methods from satellite (section 4) and ground-based (section 5) remote sensing.
7-μm channel are less impacted by subpixel heterogeneities than when retrieved from the 2. 1-μm channel. A related problem to subpixel variability in cloud properties is that cloud-free regions are likely to exist within the scale of larger satellite pixel footprints (e. g. , within 1 km for MODIS), whereas retrievals generally assume a fully cloudy pixel. The presence of cloud-free regions could be considered as being similar to subgrid  c variability within a pixel with the cloud-free regions having zero  c and thus very low visible reflectances. However, the cloud-free regions are also likely to introduce some very high SWIR reflectances too (i. e. , the value corresponding to the surface). 10. 1029/2017RG000593  Leahy et al. (2012)  give an idea of the likely scale of this problem by using colocated satellite cloud lidar data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO;  Winker et al.
N d Satellite Intercomparison: MODIS and AATSR Instruments The AATSR instrument is on board the ENVISAT satellite and observes at similar wavelength channels to MODIS and thus can be used as an alternative to MODIS for estimating N d or to provide additional data. AATSR retrieves cloud properties using an optimal estimation framework in the Optimal Retrieval for Aerosol and Cloud (ORAC) algorithm  (Poulsen et al. , 2012). Since this is different to the approach used by MODIS, differences between the two N d data sets are informative in terms of the uncertainty that is introduced by the retrieval methods. N d from MODIS on Aqua (using both Collection 5. 1 and Collection 6 data, hereafter C5 and C6, respectively) and AATSR are examined using 3 months of daytime observations during June, July, and August (JJA) of 2008 (Figure  10 ).
The C5 data set is based upon GW14 as above except without the filtering for cloud fractions less than 80%. The C6 data set is BR17. See the figure caption for more details. to the MODIS C5 product by approximately 7%. The large regional differences, particularly in stratocumulusdominated locations are primarily due to larger cloud optical depths (by approximately 5) in ORAC-AATSR observations (not shown).
Because this effect concerned the unresolved heterogeneity of cloud properties, it is difficult to directly correct through improvements of the retrieval methods. However,  Zhang et al. (2016)  predict the expected subpixel biases on  c and r e based on a knowledge of the subpixel variability of R vis and R SWIR. A Taylor expansion approach is used to compute the nonlinear relations between cloud properties and reflectances through derivative calculations and predict their subsequent effects 10. 1029/2017RG000593 on cloud retrievals. Zhang et al. (2016)  demonstrated agreement between simulated and measured biases in MODIS retrievals, but only for one scene and using relatively coarse resolution (500 m) data to analyze the subpixel effects. Werner et al. (2018)  extended the analysis using 30-m resolution ASTER retrievals of  c and r e to assess subpixel biases for 48 stratocumulus scenes and compared them to those predicted using the method of  Zhang et al.
(2016). Again, good agreement was found with correlation coefficients of 0. 97 for the predicted  c bias and 0. 8 for the r e bias. This method thus seems adequate for correcting subpixel heterogeneity errors. A difficulty is that it requires knowledge of the variances and covariances of R vis and R SWIR. While the subpixel heterogeneity over 1-km regions for the R vis channels (based on 250-m resolution reterievals) is an operational product of MODIS C6, the corresponding value for R SWIR is not. However, the results of  Werner et al. (2018)  also hint that such low-resolution data may be reasonably adequate for this purpose. Finally, section 2. 4. 6 mentioned the strong impact of multilayer conditions on liquid cloud retrievals when assuming a single cloud layer in the retrieval. A solution to this problem consists of allowing for a second cloud layer in the retrieval, and for example, applying variational methods  (Rodgers, 2000).