dataesr/scientific-paragraphs-categorization huggingface dataset:
Kulaga-Yoskovitz dataset For this dataset, we estimated again the optimal value for the 4 multi-scale mixing coefficients (3 structures + background) being α=[0. 4, 0. 5, 0. 5, 0. 9]. These coefficients have been optimized in the same way that Winterburn ones doing a Leave-Ten-Out Cross-Validation consisting of 5rounds of optimization leaving five cases (and its flipped version) out (5 rounds of 40 subjects for optimization and 10 subjects for test). The result were 5 sets of coefficients that we used in the following experiments. For the sake of simplicity, the a values provided correspond to the mean of the 10 optimization rounds as done in  (Romero et al. , 2017). In table 2, it can be seen the results (measures using a using also a LFOCV) comparing both versions of the mono-contrast method (T1w and T2w) and the multi-contrast version based on SSD and MSN.
Systematic error corrector Finally, we evaluated our proposed error corrector and compared it with the SegAdapter method  (Wang et al. , 2011). For this comparison, we used the 1. 9 version of the SegAdapter method with optimal parameters empirically obtained. For the Winterburn dataset, we used a dilation radius of 1 to obtain the uncertainty ROI, a sampling rate of 0. 15, a patch size of 7x7x7 voxels and T1w and T2w images as features. For Kulaga-Yoskovitz dataset, we used also a dilation radius of 1, a sampling rate of 0. 1, a patch size of 7x7x7 voxels and T1w images as features (T2w images did not help the correction in this dataset). For the Winterburn dataset, we trained both SegAdapter and PEC method 5 times in a leave-twoout cross validation (L2OCV) strategy by removing each pair of hippocampus (left and right) from each case being evaluated.
For the Kulaga-Yoskovitz dataset, a L2OCV would result in 5 labels x 25 subjects = 125 training rounds which supposes several weeks of processing so we performed only two training rounds splitting the dataset in two groups with 15 and 10 subjects and cross validated them. Tables  3  and 4  show the DICE coefficient achieved by the correction methods over both datasets. Note that PEC performed well for both datasets. However, the increment obtained for Winterburn dataset was higher. This makes sense since Winterburn results had more room for improvement than Kulaga-Yoskovitz (0. 6943 against 0. 8826) and the library size was quite small. Also, Winterburn protocol structures are smaller in its definition than Kulaga-Yoskovitz ones so small changes have a greater impact in DICE coefficient. The improvement provided by segAdapter was quite small for the Kulaga-Yoskovitz dataset while it had a negative impact with the Winterburn dataset.
This was counterintuitive so extensive experiments were performed to assess the validity of the conclusions. We can only suppose that maybe SegAdapter method is not well adapted to HR data as it was developed for standard resolution data and also that the small number of training data (Winterburn dataset mainly) may introduce some overfitting problems. We also evaluated the methods excluding a subset of labels from the correction process. We found that PEC performs better over the Kulaga-Yoskovitz dataset by excluding the CA4\DG so the correction is not applied to this structure. We didn´t apply this selection on SegAdapter method since this method requires all labels to be corrected at the same time (no partial correction is allowed).
Tables  5, 6 , 7 and 8 show the results for both datasets. The results confirm that HIPS can produce competitive results when using standard resolution images. Note that the results using LASR are better than using B-spline interpolation for the Kulaga-Yoskovitz dataset. However, this improvement is small despite the statistical significance. Moreover, the segmentation accuracy obtained using images up-sampled with LASR is close to the accuracy obtained using HR data as no significant differences were found. This important result shows that the proposed framework can efficiently process usual 1x1x1 mm 3 MR data. On the other hand, one may think that the method is using heavy prior information instead of following the anatomical references contained in the image. Our analysis of the results suggest that this is not the case because just looking at the SR data, image features such as the hippocampus "dark-band" (T1) are visible after the up-sampling process (see figures 3 and 4).
Methods comparison We compared the proposed method HIPS with other recent methods applied to hippocampus subfield segmentation over both datasets. We compared HIPS results on the Winterburn dataset with MAGeT results  (Pipitone et al. , in 2014)  as reported by the authors. We also compared HIPS results on Kulaga-Yoskovitz dataset with ASHS  (Yushkevich et al. , 2015b)  and SurfPatch  (Caldairou et al. , 2016). We used the published results of ASHS and SurfPatch as provided in  (Caldairou et al. , 2016)  in the comparison. We also included human rater information from Winterburn dataset original paper  (Winterburn et al. , 2013)  and Kulaga-Yoskovitz dataset original paper  (Kulaga-Yoskovtiz et al. , 2015)  as a reference. Table  7  shows results for MAGeT and HIPS on the Winterburn dataset while table  8  shows results for ASHS, SurfPatch and HIPS on the Kulaga-Yoskovitz dataset.
Discussion One of the contributions of this work is a new multi-contrast patch similarity consisting on a multicontrast SSD-based semi-norm (MSN). Introducing this similarity measure in OPAL (now MOPAL), we achieved good segmentation results using T1w+T2w images. By using the semi-norm to combine distances we obtain a robust and self-balanced similarity that takes benefit from information coming from both channels. This means image corruption or low image quality in one of the channels can be overcome using the proposed similarity properties. This contribution makes the method more robust especially when applied to different datasets/conditions. In addition, we proposed a new systematic error correction method using an ensemble of patchbased neural networks (PEC). The use of this error corrector significantly improves the results over both datasets having an execution time overload of just a few seconds.
Even though both are ensemble methods, PEC has shown to perform better that SegAdapter when using a significant lower number of base classifiers. Both methods use a boosting technique to learn the misclassified patterns. The main difference of PEC is the use of patch-based strong classifiers instead of weak classifiers as done by SegAdapter jointly with a richer feature descriptor. We chose a neural network base classifier because its versatility and availability to perform structured prediction (patch correction vs voxel correction) enhancing label regularity. Furthermore, the chosen classifier strength allowed to converge rapidly needing only 10 networks to reach the maximum accuracy. It is worth to note that using this correction, the proposed method almost reaches human rater accuracy for the Kulaga-Yoskovitz dataset where obtained a higher DICE than the inter-rater for the CA1-3 (0.
9030 obtained by HIPS versus 0. 8760 obtained by the inter-rater), almost the same accuracy for the Subiculum (0. 8705 obtained by HIPS versus 0. 8710 obtained by the inter-rater) and presents an overall dice of 0. 8744 which is considerably close to the 0. 8833 obtained by the inter-rater. Comparing the results obtained in both datasets, it is expectable to see an improvement over the Winterburn dataset if more manually delineated cases were released. Even although this is heavily dependent on the application, we think (based in our previous works using multi-atlas segmentation) that optimal results can be obtained using at least 20 reference atlases. We plan to add new manually labeled cases to the library to further leverage the method results. HIPS pipeline will be made publically available to the scientific community as part of our online volBrain platform (http://volbrain.
Conclusion In this work, we have presented a new method for HR hippocampus subfield segmentation called HIPS. It uses two publically available segmentation protocols and datasets  (Winterburn and Kulaga-Yoskovitz). Our method is based on MOPAL, a multi-contrast extension of the OPAL patchbased label fusion segmentation method and a novel neural network based error corrector. HIPS works in a fully automated manner providing accurate results in less than 20 minutes thanks to MOPAL that performs fast segmentation as well as to the subject specific library registration that only requires estimating one non-linear registration over small-region to translate the whole library to the case to be segmented. Furthermore, as it has been shown, our proposed method is able to produce competitive results on standard resolution images.
This is an important feature as it makes the method a suitable tool for standard resolution data analysis and opens the door to analyze large legacy databases. This contributes to the method scalability as well as the use of a library of manually labeled images as knowledge base. The system can learn new anatomy patterns just by extending this library with new segmented cases. This way the method can be either extended or adapted to a specific dataset or pathology. From the robustness point of view, the registration is a key step. For this reason we used a multicontrast registration as well as multi-contrast segmentation. This way we covered more variability and reduced the results dispersion in terms of accuracy especially over the Kulaga-Yoskovitz dataset. We showed that HIPS outperforms other state-of-the-art methods in term of segmentation accuracy achieving an overall DICE of 0.
661 for the Winterburn dataset while MAGeT  (Pipitone et al. , 2014)  obtains a DICE of 0. 5260 and an overall DICE of 0. 8744 for Kulaga-Yoskovitz while ASHS  (Yushkevich et al. , 2015b)  obtains 0. 8513 and SurfPatch  (Caldairou et al. , 2016)  obtains 0. 8503. HIPS is also faster than the other methods taking an average execution time under 20 minutes compared to several hours required by the other methods. It is also important to note that HIPS performance, for the Kulaga-Yoskovitz dataset, is close to the human rater reaching better results than the inter-rater segmentation for the CA1-3 sctructure. This does not happen for the Winterburn dataset which can be explained by the low number of manually labeled cases (only 5 subjects) as well as the higher structural complexity of the segmentations.
Mean DICE in the MNI space and standard deviation for each structure segmentation using high resolution T1w, T2w and Multi-contrast respectively over the Winterburn dataset. Best results in bold. Kurskal-Wallis test revealed that for each row (Average, structures and Hippocampus) there exist differences. Significant differences are marked with * for T1w and T2w, Ɨ for T1w and T1w+T2w MSN, ǂ for T2w and T1w+T2w and φ for T1w+T2w SSD and T1w+T2w MSN (p< 0. 05).
Mean DICE in the MNI space and standard deviation for each structure segmentation using the high resolution library applying B-spline interpolation and LASR to the previously downsampled image to be segmented. Segmentation produced using the multi-contrast version of the method over the Winterburn dataset. No significant differences were found between B-spline and LARS,B-spline and HR and LASR and HR. Best results in bold. Results using the HR images are also provided for comparison.
Mean DICE in the MNI space and standard deviation for each structure segmentation using the high resolution library applying B-spline interpolation and LASR to the previously downsampled image to be segmented. Segmentation produced using the mono-contrast (T1w) version of the method over the Winterburn dataset. No significant differences were found between B-spline and LARS, B-spline and HR and LASR and HR. Best results in bold. Results using the HR images are also provided for comparison.
Mean DICE in the MNI space and standard deviation for each structure segmentation using the high resolution library applying B-spline interpolation and LASR to the previously downsampled image to be segmented. Segmentation produced using the multi-contrast version of the method over de Kulaga-Yoskovitz dataset. Kurskal-Wallis test revealed tha there exist differences for the average DICE, the CA1-3 and the whole hippocampus. Significant differences are marked with * for B-spline and LASR, Ɨ for LASR and HR and ǂ for B-spline and HR. Best results in bold. Results using the HR images are also provided for comparison.
Mean DICE in the MNI space and standard deviation for each structure segmentation using the high resolution library applying B-spline interpolation and LASR to the previously downsampled image to be segmented. Segmentation produced using the mono-contrast (T1w) version of the method over de Kulaga-Yoskovitz dataset. Kurskal-Wallis test revealed that there exist differences for the average DICE and the CA4/DG. Significant differences are marked with * for B-spline and LASR, Ɨ for LASR and HR and ǂ for B-spline and HR. Best results in bold. Results using the HR images are also provided for comparison.
Acknowledgements This research was supported by  Spanish   UPV2016-0099  and  TIN2013-43457 -R grants from  UPV  and the  Ministerio de Economia y competitividad. This study has been carried out with financial support from the  French State , managed by the  French National Research Agency (ANR)  in the frame of the Investments for the future  Program IdEx Bordeaux  ( ANR-10-IDEX-03-02 ,  HL-MRI  Project),  Cluster of excellence CPU  and TRAIL ( HR-DTI ANR-10-LABX-57 ) and the  CNRS  multidisciplinary project " Défi imag'In ". We also want to thank  Javier Juan Albarracín  for his valuable contribution to the development of this method.
The objective of this paper is to present the role of Ontology Learning Process in supporting an ontology engineer for creating and maintaining ontologies from textual resources. The knowledge structures that interest us are legal domain-specific ontologies. We will use these ontologies to build legal domain ontology for a Lebanese legal knowledge based system. The domain application of this work is the Lebanese criminal system. Ontologies can be learnt from various sources, such as databases, structured and unstructured documents. Here, the focus is on the acquisition of ontologies from unstructured text, provided as input. In this work, the Ontology Learning Process represents a knowledge extraction phase using Natural Language Processing techniques. The resulted ontology is considered as inexpressive ontology.
Input As aforementioned, ontologies can be learnt, by applying the OL process, from various sources of data types: structured (such as databases), semistructured (e. g. XML) and unstructured textual documents. The domain application of this work is the Lebanese criminal code which is an unstructured text resource. This type of resources is the most available format as input for ontology learning processes. They reflect mostly the domain knowledge for which the user is building the ontology. In addition, they describe the terminology, concepts and conceptual structures of the given domain. However, some authors, such as  (Rogger, 2010) , consider that processing unstructured data is the most complicated problem because most of the knowledge is implicit and allows conceptualizing it by different people in different manner.
Text2Onto is built upon the GATE 1 framework. Accordingly, Text2Onto implements linguistic processing and machine learning statistical techniques to extract domain concepts and relations. This tool features also algorithms for generating concepts, taxonomic and non-taxonomic relations. OntoGen is a semiautomatic and data-driven ontology editor that helps the users to build ontologies by suggesting concepts and relations. This system integrates machine learning and text mining algorithms. OntoGen offers two main features: concept suggestion and naming and ontology and concept visualization. T2K 2 extracts domain-specific information from texts using natural language processing techniques in three main phases: preprocess text and extract terms, form concepts using POS patterns and relations or knowledge organization (Dell'Orletta, 2014).
What differs our work first is the domain application, which is the Lebanese criminal code. The context of the code is composed of legal norms written in legal language. Secondly, we used Text2Onto to extract all the essential elements of a domain-specific ontology. Finally, we expect to build an expressive domain-specific ontology for reasoning system, which is difficult using only an ontology learning tool, for this reason we have proposed a reengineering approach, based not only on online ontologies, to correct the errors and to enrich the extracted ontology with relations and axioms in order to make it more expressive.
METHODS We have performed a retrospective study including a total of 179 consecutive patients with gastric cancer (GC) submitted to gastrectomy during 2011 at the Portuguese Oncology Institute of Porto. Clinical and pathological data was collected from individual clinical records and inserted on a database with unique codification. Tumour tissues were collected from the institutional tumour bank. ebV was detected by in situ hybridization for the detection of ebV-encoded small RNAs (ebeRs) and ebV latent proteins (LMP1 and LMP2A) were detected by immunohistochemistry.
Study description We have developed a retrospective study in a cohort of 179 consecutive patients diagnosed with gastric cancer and submitted to surgery at the Portuguese Institute of Oncology of Porto (IPO Porto FG EPE) in 2011. Inclusion criteria: (1) patients with histologically confirmed gastric cancer; (2) submitted to gastrectomy (total or partial); and (3) with representative tumour blocks for adequate evaluation of EBV presence. All tumour samples were histologically examined by a senior pathologist and classified according to the WHO classification system  [10, 14]. All clinical and pathological data was collected from individual clinical records and inserted on a database with unique codification. TNM-staging was performed in accordance to the UICC/AJCC system 7 th edition  [14].
Statistical analysis Statistical analysis was performed using the computer software IBM SPSS statistics for Macintosh, version 20. 0 (IBM Corp, Armonk, NY, United States). χ 2 or Fisher Exact-test was used to compare categorical variables, with a significance level of 5%. Overall survival was defined as the time between the date of surgery to the date of last follow-up (censored) or the date of patient death (event). Cases lost to follow up and those ending in death from any cause other than gastric cancer were regarded as censored data during the analysis of survival rates. The differences in survival times between subgroups were calculated using the log-rank test and the Kaplan-Meier method was used to calculate survival probabilities.
revised and collaborated in the manuscript writing and final preparation. Supported by this article was supported by  FEDER  through the operation  POCI-01-0145-FEDER-007746  funded by the  Programa Operacional Competitividade e Internacionalização -COMPETE2020  and by  National Funds through FCT -Fundação para a Ciência e a Tecnologia  within  CINTESIS, R&D Unit  (reference  UID/IC/4255/2013 ). Joana Ribeiro  has been granted with a PhD Scholarship ( SFRH/BD/107740/2015 ) from  FCT -Fundação para Ciência e Tecnologia. Institutional review board statement: All procedures were approved by the  Ethical Committee of IPO Porto  ( CES IPO 80/2014 ). required to give informed consent to this study. The study included samples from the institution tumour bank of patients with cancer after each patient agreed to treatment by written consent.
56 to 2090. 62 µg/cm 2 , 45. 78 to 885. 08 µg/cm 2 , 579. 88 to 1159. 76 µg/cm 2 , 30. 52 to 198. 38 µg/cm 2 , 76. 3 to 1083. 46 µg/cm 2 , 1007. 16 to 7980. 98 µg/cm 2 for Round-up, Stratos Ultra Jardin, Polyflor, Capiscol, Polyvalent and KB Limace, respectively. The RAD values ranges of Round-up GT Plus (91. 56 to 198. 38 µg/cm 2 ) and Stratos Ultra Jardin (30. 52 to 61. 04 µg/cm 2 ) being close or including their respective LC 0 values, reinforce the thesis of non-use at important rates of these pesticides. These data constitute helpful information for database about chemical toxicity and agro-industry pesticides.
For the simulations we use the Geant4 package  [50] , using a detailed description of the geometry of the set-up (the DTAS spectrometer, the ancillary detectors and the beam pipe). Moreover, the MC simulations include the non-proportionality of the light yield in NaI(Tl) in the form described in  [36]. The inclusion of this process has been shown to be crucial  [36, 44]  in the analysis of TAS data obtained with spectrometers made of this material. The Geant4 MC simulations were validated for this geometry by comparison with measurements of well-known radioactive sources ( 24 Na, 60 Co, 137 Cs, 22 Na, and 152 Eu-133 Ba)  [44]. In addition, we investigated the sensitivity of the βdetector to γ-rays. This can introduce distortions in the β-gated TAGS spectrum. For a realistic estimation, we made a MC simulation with decay cascades generated with the DECAYGEN event generator  [38].
387 MeV, where the last level populated in the continuum is at 2. 940 MeV. The level at 2. 934 MeV, according to the information from the Evaluated Nuclear Structure Data File (ENSDF), de-excites with a single γ-ray of 2934 keV to the ground state and has spin-parity values of 1 + or 2 +. In the RIPL-3 database  [45] , a spin-parity assignment of 2 + is suggested and this was our assumption in the analysis. However, we also tested the 1 + spin-parity assignment as a possibility for this level, with very similar results. The final β intensity distribution was obtained with the known level scheme up to 3. 072 MeV, and it is presented in the fourth column of Table  II. The quality of the final analysis is shown in Figure  7. Relat. Dev.
The new TAGS data give a slightly larger ground state feeding inten- sity (< 1% difference) and a population of the first 2 + state ∼ 50% smaller. Intensities to levels at 2. 099 MeV and 2. 241 MeV have relative differences with ENSDF of ∼ 40% and ∼ 50% respectively. These levels are weakly populated, and the β intensities are determined with large errors in our analysis. Similarly, the intensity to the level at 2. 660 MeV also has a large error because it is strongly affected by the 2614. 5 keV peak in the environmental background, and the intensity differs by ∼ 30% from the evaluated value. Concerning the most important branch of the decay, the ground state feeding, we obtained a value of 93. 9(5)%, in comparison with the 93. 3(1)% value from ENSDF  [53]. However, it should be noted that the quoted value in ENSDF has a quite small error.
ACKNOWLEDGMENTS AA acknowledges useful discussions with  Prof. A Garcia  and  Dr. S Sjue  during the preparation of the experimental proposal. This work has been supported by the  Spanish Ministerio de Economía y Competitividad  under Grants No. FPA2011-24553 , No. AIC-A-2011-0696 , No. FPA2014-52823-C2-1-P , No. FPA2015-65035-P , No. FPI/BES-2014-068222  and the program  Severo Ochoa  ( SEV-2014-0398 ), by the  Spanish Ministerio de Educación  under the  FPU12/01527 Grant , by the  European Commission  under the  FP7/EURATOM  contract  605203  and the  FP7/ENSAR  contract  262010 , and by the  Junta para la Ampliación de Estudios Programme (CSIC JAE-Doc  contract) co-financed by  FSE. This work has been partially supported by the  Academy of Finland  under the  Finnish Centre of Excellence Programme 2012-2017 (Project No.