dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This research was funded by both the  RGC Early Career Scheme   2630020  and the  Center for Ocean Research in Hong Kong and Macau , a joint research center between the  Qingdao National Laboratory for Marine Science and Technology and Hong Kong University of Science and Technology. The authors thank the chief editor  Stephen Griffies  and the anonymous referees for their comments that resulted in a presentation and scientific improvement of the manuscript. JM would additionally like to thank  Xiaoming Zhai ,  James Maddison  and  David Marshall  for various technical comments on the various iterations of the present work.
Data Availability Statement For the pre-processing, NEMO ORCA0083-N01 data was obtained from http://gws-access. jasmin. ac. uk/public/ nemo/, provided by UK National Oceanography Center through the JASMIN service. The base version of CDFTOOLS was taken from https://github. com/meom-group/CDFTOOLS. For the optimization calculations, this work uses a natively installed version of Firedrake, with a mesh generated via the Qmesh package (https:// www. qmesh. org/) via a the Firedrake wrapper of tlm_adjoint (https://github. com/jrmaddison/tlm_adjoint). The post-processing analysis uses standard Python packages. Modifications of CDFTOOLS, the relevant Python scripts, the processed data from this work (in both the native finite element as well as the various gridded versions), and documentation of software, versions and its dependencies are available on http://dx.
This paper aims to analyse the percentage of gender bias resulting from the use of Google Translate, DeepL, and SmartCat when translating raw in-game content from English into French. The games DeltaRune, The Devil's Womb and The Faces of the Forest were chosen due to the presence of non-binary characters, nonsexualized characters, and female protagonists. We compared the results in order to recount and analyse the differences between these tools' output in terms of errors related to gender. To this end, we created a parallel corpus to compare source documents and each translation to visualise the semantic and grammatical directions of the word embeddings  [Zhou et al. , 2019]  and extracted the collocations and concordance lines that represented gender identity by analysing the patterns in the source language.
Systems We decided to analyse the output of Google Translate and DeepL-NMT-based-as well as SmartCat (to represent Statistical Machine Translation (SMT) tools). As the company explains in their blog [Google AI Blog], Google Neural Machine Translation (GNMT), which is integrated into Google Translate, was developed in 2016 and modified in 2020. It provides language detection and includes various technologies based on Artificial Intelligence that allow it to handle 100+ languages. GNMT uses a hybrid model architecture that replaces the previous RNN-based GNMT model-used to identify data correlations and patterns-with a transformer encoder (that reads the source text and searches for correlations) and an RNN decoder for predictive typing [Google AI Blog]. These hybrid models improve the quality and lower the time of latency.
Additionally, the 2020 update allows Google to web crawl using an embedding-based model rather than the previous phrase-based machine translation model of 2016 (dictionary-based crawler), thus improving the quantity and quality of the data and increasing the number of collected sentences [Google AI Blog]. Nevertheless, using parallel (or translated) texts as the linguistic data used in its training phase subjects the availability and quantity of paired texts and languages to their presence on the web, affecting the translation quality. Additionally, GNMT will be more accurate at translating terms or words from a source language to a target language rather than long sentences, entire texts, or complex idioms  [Yang et al. , 2020]. Once again using the company's own description [DeepL Blog], DeepL uses Deep Learning and Neural Networks and draws the linguistic data used for its training from Linguee, a massive corpus built around manually translated sentences, idioms, and texts.
The quantity and diversity of the data utilised account for DeepL's degree of accuracy and performance in specialised domains and its capacity of offering translation alternatives such as synonyms and rephrased options [DeepL Blog]. Additionally, DeepL includes predictive typing which, combined with the translation alternatives function of DeepL, works similarly to translation memories and means that DeepL might be able to memorise the user's translation alternative when the latter changes a term or rephrases the entire sentence. In consequence, the user's alternative would increase the amount of data exploited by DeepL, which already uses Deep Learning to constantly train paired languages, allowing it to train with it and increasing the number of translation alternatives. In terms of MT, DeepL uses an API to provide restricted access to a glossary of terms which increases the accuracy.
In addition, this API also limits the length of documents uploaded into DeepL meaning that the text is split into sentences, then translated and finally put back together [DeepL Blog]. Finally, as a representative of MT integrated into Computer Assisted Translation (CAT) tools and an example of SMT we chose SmartCat, which uses Yandex Free by default. This system is not based on translated texts or language rules but upon statistics, which means that the tool uses comparable corpora. SMT analyses source documents in one language and statistically compares them with related documents in another language first; then it gathers, relates, and aligns the data with parallel texts [Yandex Blog]. Another difference is that the system can easily and quickly be adapted when a language faces modifications (spelling, usage, new terms or idioms), which also means that it must be regularly updated to improve its quality.
The bibles were used when the text was ambiguous and we needed to use walkthroughs to determine the identity of the speaker or the addressee. Image 1. Excel files and character bibles. Then, a colour code was created to manually annotate gender-related issues by highlighting the affected cells to provide a visual representation of the errors: • Blue: use of the masculine for a reference to a female character. • Red: use of the feminine for a reference to a masculine character. • Violet: references to non-binary or non-gendered characters. • Green: wrong use of the masculine for a reference to an object. • Orange: wrong use of the feminine for a reference to an object. Finally, we proofread, annotated, and reviewed individually at least once every document and then went together through them once more before removing all the lines that did not contain gender-related issues.
Colour code The previous image (Image 2) already shows patterns regarding the type of error and tool: in the case of blue and green errors, we can easily observe a higher number of cells without issues in the column containing DeepL's translation. Conversely, the same tool appears to err more than the rest in the other three categories. If we look at the total number of errors per tool all categories confounded (Table  1 ), Google made 333 gender-related errors, DeepL made 261, and SmartCat 340. These figures represent 3. 03%, 2. 38% and 3. 1% of the 10 980 segments, respectively. However, it is necessary to point out that SmartCat's translation quality was remarkably low and, thus, we were not able to distinguish the type of mistake made on numerous occasions. Therefore, the percentage of Yandex's errors should be much higher than the one listed above.
CONCLUSION Creating this corpus allowed us to compare the use of gender in the source texts with the raw output of various MT systems to visualise the semantics, syntax, and linguistic differences between a neutral language such as English and a gendered language. By cross-referencing the information extracted from this corpus, we were able to determine the efficiency of each tool, how they analyse the context, their accuracy, and the quality of their translation. As such, DeepL has proved to avoid many gender biases while being more efficient than Google and SmartCat. In addition, even if Google uses NMT technology, our analysis shows that its architecture and models are closer to SmartCat's. On the other hand, DeepL seems more accurate and fluent, surpassing Google thanks to its use of Deep Learning, as we have seen across the paper.
Moreover, assuming a model m of system s has been checked and needs to be updated to integrate new requirements, what will be the price of formally verifying an updated model m of s? In other words, could we avoid entirely re-verifying m ? This paper positively answers to these questions. Again, the proposed solution relies on dependency graphs. This paper sketches the dependency graph generation algorithm and relies on a case study to show the efficiency of its implementation in the free software TTool  (TTool, 2021; de Saqui-Sannes et al. , 2021) , both for the proof of reachability and liveness properties. Section 2 formally defines a subset of SysML. Section 3 introduces dependency graphs. It also presents the algorithms implemented by TTool to generate dependency graphs from SysML models.
Formal Verification with TTool TTool  (TTool, 2021; Apvrille et al. , 2020 ) is a free and open source framework for the design and verification of embedded systems. The TTool model checker  (Calvino and Apvrille, 2021)  inputs SysML models enriched with safety properties to be verified and outputs a yes-no answer for each property. In practice, the TTool model checker takes as input (1) a block instance diagram and the state machine diagrams modeling the inner workings of the blocks, and (2) properties formally expressed using a CTL-based language.
Specifically, KGs are typically used for representing auxiliary knowledge either in an underlying graph-structured schema or in a vector-based KG embedding. The survey also provides an overview of KG embedding methods and describes several joint training objectives suitable to combine them with high dimensional visual embeddings. The third paper, "Tab2KG: Semantic Table  Interpretation  with Lightweight Semantic Profiles"  [7]  by Simon Gottschalk and Elena Demidova, presents Tab2KG, a new method for automatically inferring tabular data semantics and transforming such data into a data graph. This solution uses a one-shot learning approach that relies on lightweight semantic profiles to map a tabular dataset containing previously unseen instances to a domain ontology. Tab2KG outperforms state-of-the-art semantic table interpretation baselines on several real-world datasets from different application domains.
The fourth paper, "Answer Selection in Community Question Answering Exploiting Knowledge Graph and Context Information"  [2]  by Golshan Afzali Boroujenia, Heshaam Failia, and Yadollah Yaghoobzadeha present a novel answer selection method that takes advantage of the knowledge embedded in KGs. This solution uses variational autoencoders in a multi-task learning process with a classifier to produce class-specific representations of the answers. The method outperforms significantly the existing baselines on three widely used datasets. The fifth paper, "Network representation learning method embedding linear and nonlinear network structures"  [20]  by Hu Zhang, Jingjing Zhou, Ru Li, and Fan Yue proposed an enhancement to the Hierarchical Graph Convolutional Networks model to learn network representations.
The seventh paper, "Analyzing the generalizability of the network-based topic emergence identification method"  [9]  by Sukhwan Jung, and Aviv Segev, analyzed the topic evolution method with the task to predict new topics. The method is general and can work in any collection of data where the topics are defined by their neighbors' previous relationships. Twenty sample topic networks were built within different domains such as business, materials, diseases, and computer science from the Microsoft Academic Graph dataset. The eighth paper, "Knowledge Graph Embedding for Data Mining vs. Knowledge Graph Embedding for Link Prediction -Two Sides of the same Coin?"  [17]  by Jan Portisch, Nicolas Heist, and Heiko Paulheim, examines two tasks: encoding provision for data mining tasks and predicting links in a KG.
The data for the customer demand in 12 periods, are provided in Table  5. Results of our algorithm for small test problems are given in Table  6. It is evident that the optimality gap in comparison with the CPLEX software is very low and in most of test problems, our Lagrangian relaxation algorithm finds the optimal solution. It should be noted that the gap between the lower and upper bounds of the proposed algorithm is also low and this shows that our heuristics are also successful to find an efficient solution in small-scale instances. From our results given in Table  8 , the proposed algorithm could solve the model optimally with limited number of iterations. This statement can be concluded when we discover that the maximum deviation between the optimal and near optimal solutions in worse case is 22% in small-scale instances and 42% in large-scale instances.
The viability of a sustainable CLSC in a real-life case study in Iran was demonstrated by the results. The constraints of the proposed CLSC network support the inventory statuses, the flows of materials, components and products, capacity limitations and the location of facilities. To handle these constraints, the most difficult ones were relaxed and our Lagrangian algorithm outperforms the CPLEX software (Tables  6  and 8 ; Fig. 2 ). The proposed algorithm is able to achieve the feasible optimal solution in small instances and its optimality gap is low even in large-scale instances. To show the validation of the proposed algorithm, the proposed algorithm is compared with the exact solver obtained by IBM ILOG CPLEX software. For our small tests, the CPU time for our algorithm in comparison with the CPLEX software, is depicted by Fig.
2. It goes without saying that the computational time of our heuristics are less than one second. Their CPU time is considered for the computational of our proposed Lagrangian algorithm. From this chart, it is evident that our algorithm is highly faster than the exact solver using CPLEX software  This paper contributed to an active topic in the area of SCND by proposing an efficient and sustainable CLSC considering service levels, energy consumption and job opportunities. The main new assumption of our model was to consider five activities for distribution centers for the role of warehouses and collection centers. Our CLSC network include suppliers, manufacturers, distribution centers, customer zones, and disposal centers. The proposed model also addressed the customer demand with no shortage option.
The proposed CLSC model made location, allocation and inventory decisions to optimize the total profit, energy consumption and job opportunities, simultaneously. Another novelty of this research was to develop a set of efficient Lagrangian relaxation reformulations and two problem-specific heuristics for solving a real-world numerical example. The results have revealed that the obtained solution is feasible and the developed solution algorithm is highly efficient for solving supply chain models. To validate our solutions, we have used the exact solver using CPLEX software. Finally, a comprehensive discussion was provided to highlight our findings and managerial insights from our results. One main finding confirms that simultaneous consideration of service levels, job opportunities and energy consumption make our sustainable CLSC more realistic and our Lagrangian algorithm can be applied to other complex SCND models.
Data Analysis ICC estimates and their 95% confident intervals were calculated using SPSS statistical package version 23 (SPSS Inc. , Chicago, IL, USA) based on an absolute-agreement, single measurement, 2-way mixed-effects model. We compared the total scores of the MMSE (from 0 to 30). For the FCSRT, we compared the total recall score (0-48), the total free recall score (from 0-16), and the delayed recall score (0-16). For the naming task (LEXIS), we compared the total score (0-64). We compared reading's duration of the three subitem for the STROOP test (color, reading, interference). For verbal fluencies, we compared the total number of words said for the semantic and phonemic verbal fluencies. Due to our sample's small size, we conducted a non-parametric Wilcoxon paired sample test for a group comparison.
Funding: This work is supported by the European Institute for Innovation and Technology (EIT)-Health, 'DeepSPA' grant number 19249 with great support from Modis Belgium, Janssen Clinical Innovation. This research was partly funded by the MEPHESTO project (DRI-0120105/291). The project received funding from the Interreg-Alcotra project CLIP "E-SANTÉ / SILVER ECONOMY".
, 2010) , the lack of a morphological survey of GCs has hindered a detailed description of the postnatally developing cerebellar cortex. To shed light on this gap, we examined the EGL of a developing mouse cerebellum (lobule VIII) using a serial-sectioning scanning electron microscopy-(ssSEM) based connectomic approach, which represents a state-of-the-art approach to study brain connectivity at nanometer resolution  (Kasthuri et al. , 2015; Wilson et al. , 2019). Obtained from a 7-day-old mouse (postnatal day 7 or P7), this dataset exhibited an expanded EGL resulting from a peak in GC proliferation at P6  (Espinosa and Luo, 2008 ) that enabled us to study a large number of GCs in the EGL. These data led to the discovery of GCs connected by membrane-bound cytoplasmic, intercellular connections (ICs) throughout the depth of the EGL.
ICs are anatomically diverse and complex In order to characterize the diversity of ICs, we developed a program for morphometric analysis of segmented cells called CellWalker that allows for calculation of morphological features of segmented cellular structures, including length, diameter, volume, surface area and curvature (see supplementary methods). Using CellWalker, we saw that IC lengths ranged from 1. 25-to 3. 3 μm with an average of 2. 25 μm (Fig. 5A ), comparable to in vitro and in vivo CBs, which exhibit lengths between 3 and 5 μm  (Mullins and McIntosh, 1982). To assess trends in the thickness (or diameter) of ICs, we sliced each IC at 50 nm intervals along their lengths to obtain a series of cross-sections (Fig. 5B ). To assist the determination of the axis for cross-sectioning, we skeletonized ICs using the Kimimaro algorithm  (Silversmith et al.
, 2022; Zenker et al. , 2017). We quantified the overall voxel intensity in each cross-section along ICs as a proxy for cytoskeletal filament density (Fig. 5B-D , see supplementary methods). Profiles obtained from intensity-scaled electron micrographs showed that ICs contained dense regions that could correspond to MBs (Fig. 5D ). Segmentation of darker voxels and 3D-navigation using the virtual reality software, DIVA  (El Beheiry et al. , 2020) , illustrate these quantitative findings and provide a realistic representation of the cytoskeletal architecture within connections (Fig. 5E ). We also identified several vesicular and membranous compartments within ICs. Segmentation of these compartments revealed volume and morphology heterogeneity, ranging from small spherical vesicles to large extended compartments (Fig.
The observation of different cargoes, including membranous compartments and mitochondria, within ICs might support this hypothesis, as persistent IBs represent a mechanism of intercellular transport distinct from conventional vesicle trafficking in MBs of CBs. Given the fixed-time-point constraint of our dataset, confirming the source of the unequal ratio of prometaphase and IC-connected cells is challenging. However, the fact that we observed neither cells interconnected as syncytia (i. e. more than 2 connected cells in a row) nor microtubulebare ICs, both of which are features observed in IBs  (Haglund et al. , 2011; Mathieu et al. , 2022) , suggests that many of the ICs we observed differ from IBs previously described in gonad maturation  (Haglund et al. , 2011). Our 3D-reconstructions revealed three specific features that differentiate ICs from classical CBs, each of which could give clues about the nature of ICs.
Acknowledgments We thank members of the  Lichtman lab  ( D. Berger ,  N. Dhanyasi ,  X. Lu , and  X. Wang ) for fruitful discussions;  Jean Livet  for critical discussions; all members of the  Zurzolo lab  for discussions and support, especially  M. Rakotobe  for critical discussions on cerebellum biology;  W. Silversmith  for early help during our initial skeletonizations;  C. Godard  for visualization of electron density using DIVA; data annotators:  Ö. Özen ,  M ,  M. Trottmann ,  O. Mulhern ,  N. Borak ,  M. Bogan ,  A. Mancevski ,  R. McLellan ,  E. Sun ,  A. Atkin ,  S. O'Shea ,  N. Sullivan ,  A. Vaca ,  C. Vargas ,  D. Pacheco ,  A. P. Lastra ,  P. Chavan ,  C. Beltran ,  J. Mamani ,  A. Flores ,  A. Meruvia ,  F. Osalinas ,  T. Chrysostomou. Internal seed grant from IP to C. Zurzolo   U19 NS104653 ,  U24 NS109102-01  and  P50MH094271   Conte Center  to  J.
connections or help in the formation of the de novo ICs. Overall, our results open several avenues of research to investigate the origin and function of ICs between developing GCs. Future research in living tissue may be required to diagnose the mechanisms underlying their formation and physiological function during cerebellar development, as well as assessing whether this phenomenon is widely present and relevant for CNS development. Inception program (Investissement d'Avenir grant ANR-16-CONV-0005) ANR-17-CONV-0005 Projet Q-life Cancéropôle Ile-de-France 2018-1-PL BIO-03-IP-1 LEGS M. MICHEL Equipe FRM Third edition of the Big Brain Theory Call.
The above observation is also supported by the graph in Fig. 13 , that gives a comparison of network eNBs edge EE performance as a function of traffic arrival rate for SCLA,  ANN, and static cases. The figure shows that in the same range of λ = 7. 14 the edge performance of eNB cells in ANN is more as compared to cells in the SCLA case. Thus a better cell edge performance gives a better data rate for users in the network. This performance improvement in ANN is due to the increase in the number of input dataset features as well as the number of input samples. From the graphs, it can be seen that both antenna tilt schemes do not deteriorate the performance with the increasing connection arrival rate but ANN responds well to the non-linearities of the system parameters of the cellular system as compared to the SCLA.
Crowdsourcing platforms enable gig workers to better identify, approach, and apply for any work assignments posted by employers, which provides them with fragmented micro-jobs  (Valenduc & Vendramin, 2017). Our aim was to understand how firms use gamification to engage their digital gig workforce, exploring this phenomenon through the lenses of swift trust theory (STT) and psychological contract theory (PCT). To do so, we collected data from organizations that used gamification in their processes to energize their employees and we analysed them to understand whether it was also being used to engage gig workers. To achieve our study's aim, we thus endeavoured to answer the following research question. "How do firms use gamification to engage their digital gig workforces and enhance their productivity?" Many people come together to achieve a common goal  (Asún, Rdz-Navarro, & Alvarado, 2016; Ballinger, Schwartz, & Andrews, 2017 ;  Barsness, Diekmann, & Seidel, 2005) , and the Internet has undoubtedly extended the reach and scale of digital gamification platforms  (Banik & Padalkar, 2021; Cascio & Montealegre, 2016; Ghosh et al.