dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental results suggest that CMs and training strategies should be adapted to a specific goal. Utterance-level detection can benefit from the use of more fine-grained information during training, whereas the comparatively more challenging task of segment-level detection calls for matched resolution. There were significant differences between the results of segment-level detection on the development and evaluation sets. We thus investigated two hypotheses to explain the differences: (1) More difficult spoofing systems exist in the evaluation set. This hypothesis is supported by a series of leave-one-out experiments for each resolution, and significant changes in the EER were observed after removing certain unknown spoofing systems. Among those unknown spoofing systems, A15 was found to be the strongest attack in the PartialSpoof dataset.
(2) Detecting spoofed segments with fewer concatenated boundaries on the evaluation set is more difficult than on the development set. This is supported by an analysis of the breakdown EERs on the spoofed data with different numbers of concatenated boundaries. The performance of the CM on the evaluation set was much worse when there were fewer concatenated boundaries within the segments. In contrast, the performance on the development set was less affected. How to overcome this issue for the segment-level detection is worth exploring in the future. We also conducted a cross-scenario study on the LA and PS scenarios. The proposed CM was shown to achieve the best reported utterance-level detection result for the ASVspoof 2019 LA database (an EER of 0. 77% for the evaluation set). The PS scenario is a realistic, important, timely, and challenging spoofing scenario, which warrants greater attention in the future.
This work was supported in part by  JST CREST  through the  Japanese-French Joint National VoicePersonae  Project under Grants  JPMJCR18A6 ,  JPMJCR20D3 , and  JPMJFS2136 , in part by the  ANR  under Grant  ANR-18-JSTS-0001 , in part by  MEXT KAKENHI  under Grants  21K17775 ,  21H04906 ,  21K11951 , and  18H04112 , and in part by  Google AI for Japan Program.
The aperture between the parallel plates is 1 mm, and the porosity is 0. 58. The field of view contains 577 pillars with an average diameter (± standard deviation) of   = 2. 8 mm (±0. 22 mm). The average pore diameter and throat width are λ = 2. 3 mm (±0. 83 mm) and a = 1. 2 mm (±0. 14 mm), respectively. The absolute permeability is 3. 38 × 10 -2 mm 2 , calculated by 2D numerical simulations of depth-averaged Stokes flow  (De et al. , 2021) , performed using the computational fluid dynamics software OpenFOAM ® (See Text S2 in Supporting Information S1). The liquid phase is a 60-40 (% weight) water-glycerol mixture. We use a 20 mg/L Fluorescein sodium salt (Sigma Aldrich, France) solution as the conservative fluorescent tracer. The mixture's viscosity and density are μ = 3. 63 × 10 -3 Pa ⋅ s and ρ = 1.
Acknowledgments Funding by the  E. U. is gratefully acknowledged by  O. Borgman ,  T. Le Borgne  and  Y. Méheust  under the  MSCA Individual Fellowship program  (project  UnsatPorMix , nr. 843594 ), and by  B. Géraud ,  T. Le Borgne  and  Y. Méheust  under the  ERC program  (Consolidator project ReactiveFronts, nr. 648377 ). O. Borgman  and  Y. Méheust  are also grateful to the  French National Agency for Research (ANR)  for funding project 2PhlowFrac (nr. ANR-20-CE92-0026 ).
The blogs were activated before data collection began, and the total number of clicks for the blogs across the two conditions was collected after the data collection procedure was completed. The number of clicks for the blogs for each condition was employed as a measure of customers' social media engagement behavior. Sample. Third, we recruited sales professionals employed in B2B firms. A total of 250 U. S. Prolific panel members meeting the screening criteria participated in the study. Of these, 66. 8% were male, 44. 0% were between 18 and 39 years old, and 52. 4% had over ten years of work experience. Measures. Finally, along with social media engagement (scale α = 0. 93) and demographic information (i. e. , age, gender, and work experience), participants responded to content-based trust (α = 0.
001, Cramer's V = 0. 56). In particular, EGC (i. e. , number of clicks = 35) resulted in a higher number of clicks than FGC (i. e. , number of clicks = 21). This finding further provided support for the effectiveness of EGC in enhancing actual social media engagement behaviors (H 1 ). We found that content-based trust (M EGC = 4. 21, SD = 0. 90; M FGC = 3. 79, SD = 1. 1. 32; F 1,248 = 8. 49, p = 0. 004, partial eta 2 = 0. 033) and engagement-based trust (M EGC = 4. 30, SD = 1. 40; M FGC = 3. 67, SD = 1. 32; F 1,248 = 13. 38, p = 0. 000, partial eta 2 = 0. 051) were significantly higher in the EGC condition than in the FGC condition. The mediating roles of content-based trust and engagement-based trust were tested using Model 4 SPSS PROCESS macro with 5000 bootstrapped resamples  (Hayes, 2018).
[Insert Fig. 3  about here]. Post-hoc analysis revealed that message source had a significant main effect on message attractiveness (F 1,248 = 4. 37, p = 0. 038, partial eta 2 = 0. 017); specifically, EGC (M = 3. 26, SD = 1. 56) was perceived as more appealing than FGC (M = 2. 85, SD = 1. 57). However, the results of the mediating analysis using Model 4 SPSS Process macro with 5000 bootstrapped resamples revealed that message attractiveness did not explain the effect of message source on social media engagement; this was because the 95% confidence intervals for the indirect effect included zero (indirect = -0. 05, SE = 0. 03, LLCI = -0. 12, ULCI = 0. 01). This finding rules out the alternative mechanism of message attractiveness in the relationship between message source and social media engagement.
62, p = 0. 000, partial eta 2 = 0. 038). No significant main effect of emojis was observed (F 1,396 = 0. 13, p = 0. 722, partial eta 2 = 0. 000). A simple slope analysis of the interaction effect revealed that, when emojis were included in the message (M = 4. 48, SD = 1. 15), EGC led to a higher level of social media engagement than when emojis were excluded (M = 4. 02, SD = 1. 22) (F 1,196 = 7. 33, p = 0. 007, partial eta 2 = 0. 036). In contrast, when FGC incorporated emojis (M = 3. 50, SD = 1. 34), social media engagement was lower compared to when emojis were omitted (M = 4. 04, SD = 1. 35) (F 1,200 = 8. 34, p = 0. 004, partial eta 2 = 0. 040). The interaction effect is illustrated in Fig. 5. [Insert Fig. 5  about here]. The mediation of content-based trust and engagement-based trust in the relationship between message source, emojis, and social media engagement was examined using Model 8 in the SPSS PROCESS Macro with 5000 bootstrapped resamples.
59, p = 0. 000, partial eta 2 = 0. 058) on engagement-based trust. No interaction effect of message source and objective information on social media engagement was observed (F 1,369 = 0. 78, p = 0. 379, partial eta 2 = 0. 002). An analysis using Model 8 SPSS PROCESS macro with 5000 bootstrapped resamples showed that objective information did not moderate the mediation of content-based trust in the relationship between message source and social media engagement. This was because the 95% confidence intervals for the index of moderated mediation (IMM) included zero (IMM = 0. 17, SE = 0. 12, LLCI = -0. 06, ULCI = 0. 40). Another analysis provided similar results for engagement-based trust. The 95% confidence intervals for the index of moderated mediation (IMM) included zero (IMM = 0. 13, SE = 0.
ACKNOWLEDGMENTS We thank  Andrey Bagrov  and  Tom Westerhout  for useful discussions. We also thank an anonymous referee for very insightful comments on the connection between our results and the standard theory of pointer states. This work was supported by the  Swiss State Secretariat for Education, Research and Innovation (SERI)  under Research Preparation Grants with Russia 2020, the project Quantum skyrmions. V. V. M. and O. M. S. also acknowledge the support from the  Russian Roadmap on Quantum Computing  (Contract No. 868-1. 3-15/15-2021 ). The work of M. I. K. was supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program , Grant Agreement No. 854843-FASTCORR. The work of E. A. S. was supported by the  European Union 's  Horizon 2020 Research and Innovation program  under the  Marie Skłodowska Curie Grant  Agreement No.
Fine-tuning with Literary Data In this section, we analyse the outputs qualitatively. This analysis consists of an initial description of the baseline and fine-tuned outputs, followed by a deeper examination of the syntactic and semantic functions of the produced outputs by both models. Instances of hallucinations were observed in the outputs of our baseline model. The hallucinated elements are present in the source text, so they are not elements which are not present in the source text. According to  Lee et al. (2018) , hallucinations can be defined as the model producing a vastly different and inadequate output when the source is perturbed under a specific noise model. Thus, we may suggest that there exist other instances where the model ceases translation of the source text and proceeds with generating output punctuated solely by a sequence of continuous commas ("""",), which may represent an alternative manifestation of hallucination.
BLEU scores In this section, we report the results of our primary, constrastive1, and contrastive2 in terms of BLEU score computed using Test1 and Test2 datasets at the end of the full training process of each model. The official results of the competition on test3 were not computed as the reference translations were not provided (at the time of writing this article). Table  1  shows that our primary system achieves the same BLEU score as contrastive2 3 , the sentence-level transformer implementation. We notice that the document-level system (contrastive1) is not better than the sentence-level model. This might be explained by the few epochs used for training.
Lexical Complexity To appreciate the relative complexity of the terms used in the translations we first qualitatively compared the translations and contrastive2 seemed to be more elaborate, so we tested this impression with more quantitative means. We investigated the vocabulary growth curves of the three translations using the functions available from the languageR package  (Baayen and Shafaei-Bajestan, 2019)  to find out that the number of different types progress on the same rhythm for the different translations. In this type of representation, the horizontal axis corresponds to the expansion of the translation corpus (number of tokens) and the vertical axis corresponds to the number of types. The first lower series of curves corresponds to the number of hapaxes. As can be seen in Figure  1 , the progression is very similar for the different translations we produced, while the mBART fine-tuning translation (primary) seems to be more verbose as the translation contains pore tokens than the two contrastive translations.
Sentence-vs. Document-based Training Strategies An important aspect of the competition was the choice to use full chapters with contextualised successive sentences instead of (more) limited contexts usually retained for translation competitions. This resulted in a much bigger dataset than for more standard competitions (in the vicinity of 400 sentences for biomedical tasks). We submitted 2 models based on a similar architecture: Contrastive1 and Contrastive2. We used as Contrastive2 a context-agnostic sentence-level transformer model as in  (Vaswani et al. , 2017)  trained on 10 epochs. We used as Contrastive1 an on-context transformer model with the exact same architecture as Contrastive2 but that adopts sliding windows of 3 concatenated sentences pre-trained on 10 epochs to the sentence-level and trained on 4 epochs with concatenated sentences.
Future Research This first collaboration between several universities and backgrounds has discussed English input and was an opportunity to discuss the findings of the competition on literary data and also our insights into the fine-tuning of mBART50 with literary data. We aim to replicate this analysis on Farsi data, as Farsi is one of the 50 languages of mBART50. As is often the case in competitions, we did not train as much as we expected. For the fine-tuning of mBART, we managed to train for three epochs, which is what we found in previous studies  (Lee et al. , 2022) , but for two other submissions, we were training from scratch and could only manage to train for 10 epochs for constrastive2 (sentencelevel) and fine-tune for 4 epochs for contrastive1 (document-level). This impacted our results.
Conclusion This paper presented the MAKE-NMTViz system description for the WMT2023 Literary Shared Task. We participated in the Chinese-to-English task with a model trained at sentence level and at document level. We only used the data provided by the organisers but also analysed the translations produced with mBART50 before our submissions. As we did not receive scores from the organisers of the task, we mostly focused on the qualitative analysis of our translations. We resorted to a typology of translation errors and highlighted prominent error types that remained in our translations.
Acknowledgements This paper emanated from research partly supported by the  MAKE-NMTVIZ  project, funded under the  2022 Grenoble-Swansea Centre  for  AI Call for Proposals/ GoSCAI -Grenoble-Swansea Joint Centre in Human Centred AI and Data Systems (MIAI@Grenoble Alpes  ( ANR-19-P3IA-0003 )), and by a 2021 research equipment grant from the  Scientific Platforms and Equipment Committee (PAPTAN project)  under the  ANR  grant ( ANR-18-IDEX-0001 ,  Financement IdEx Université de Paris ). Sadaf Mohseni  benefitted from a  Collège de France /Université Paris Cité   PAUSE scholarship  and  Nicolas Ballier  from a  CNRS  research leave at  LLF (Laboratoire de Linguistique Formelle) , which are gratefully acknowledged. This work was also supported by the  CREMA  project ( Coreference REsolution into MAchine translation)  funded by the  French National Research Agency (ANR) , contract number  ANR-21-CE23-0021-01.
ACKNOWLEDGMENTS This work was supported by the  John Templeton Foundation , Grant No. 61835. A. A. acknowledges support from the  National Research Foundation, Singapore  and  A*STAR  under its  CQT Bridging Grant , the  Foundational Questions Institute Fund  (Grants No. FQXi-IAF19-01  and No. FQXi-IAF19-05 ), and the  ANR Research Collaborative Project "Qu-DICE " (Grant No. ANR-PRC-CES47 ).
This article describes a detailed methodology for the A-phase classification of the cyclic alternating patterns (CAPs) present in sleep electroencephalography (EEG). CAPs are a valuable EEG marker of sleep instability and represent an important pattern with which to analyze additional characteristics of sleep processes, and A-phase manifestations have been linked to some specific conditions. CAP phase detection and classification are not commonly carried out routinely due to the time and attention this problem requires (and if present, CAP labels are user-dependent, visually evaluated, and hand-made); thus, an automatic tool to solve the CAP classification problem is presented. The classification experiments were carried out using a distributional representation of the EEG data obtained from the CAP Sleep Database.
Time Series Data Mining Data mining (DM) is a set of methodologies that analyze large datasets, aiming to identify patterns and relationships that can help solve different types of problems. This is achieved by four main processes: data gathering, data preparation, data mining/processing, and a final analysis and interpretation of the results  [17]. Time-series data mining (TSDM) applies these processes to sequential data, i. e. , data recorded over time  [18]. However, when analyzing some specific time series (such as biomedical signals), TSDM focuses on particular areas of interest, known as events, instead of evaluating the entire time series  [19]. Hence, this subsection describes some of the research works on TSDM that are interesting for the present proposal. Li et al. (2012)  developed a methodology to visualize variable-length time-series motifs by implementing the symbolic aggregate approximation (SAX)  [20].
None of the previous examples use sleep EEG or search for CAPs. In recent years, sleep analysis, especially the sleep staging task, has also been explored  [23, 24]. Nevertheless, the methodologies proposed to solve this task are only indirectly related to the current research, which inquires into the symbolic transformation and/or vectorization of the signals. Sleep stage classification labels 30-second-long PSG segments, known as sleep epochs. The labels are usually visually scored and divided into deep-sleep, light-sleep, and awake or, more precisely, S1 or N1, S2 or N2, S3 or N3, and R or REM, which are used to analyze sleep macro-structure. In this direction, Joe et al. (2022)  [23]  analyzed EEG and EOG images in terms of their time and frequency domains by using a CNN on the Sleep-EDFx dataset  [25].
More recently, Hartmann et al. (2019)  [28]  implemented a long short-term memory (LSTM) model to detect A-phases and classify them into three subtypes: A1, A2, and A3. From the CAP Sleep Database, they removed epochs marked as "Awake" and "REM Sleep" and worked with two patient subsets: 16 healthy patients and 30 diagnosed with nocturnal frontal-lobe epilepsy (NFLE). They achieved averaged F1 scores ranging from 57. 37% to 67. 66% in the different modalities of classification (A-phase vs. non-A-phase and A1 vs. A2 vs. A3 vs. B), as shown in Table  1. A two-dimensional convolutional neural network (2D-CNN) was implemented by Arce Santana et al. (2020)  [29]  to detect A-phases and classify them into the A1, A2, and A3 subtypes by using a different approach. They segmented nine EEG recordings in 4-second long epochs and computed their spectrograms, which are visual representations of the frequency content of a time series.
They fed the resulting images to a deep 2D-CNN and obtained mean accuracy scores of 88. 09% in A-phase detection and 77. 31% in A-phase classification (A1 vs. A2 vs. A3). Unfortunately, no other metric is reported, restricting the performance analysis within unbalanced data, which is the case of the A-phase subtypes. The automated detection of CAPs and the classification of sleep stages using a deep neural network was proposed by  Loh et al. (2021)   [30]. Six healthy patients from the CAP Sleep Database were used to segment, standardize, and classify the sleep stages and CAP patterns with a one-dimensional convolutional neural network model (1D-CNN). Loh et al. (2021) reported F1 scores of 75. 34% and 33. 04% for CAP detection in a balanced and an unbalanced dataset, respectively. Overall, the summarized metrics shown in Table  1  reveal that the model performs better on the balanced dataset than on the unbalanced dataset.
This is directly caused by the number of B-phase examples (87. 4% of the unbalanced dataset), which increased the difficulty of identifying the A-phase examples. A novel approach was explored by Tramonti Fantozzi et al. (  2021 ) to automate A-phase detection (A-phase vs. Non-A-phase) through local and multi-trace analysis  [31]. They found that channel F4-C4 performed better than all the other analyzed channels, achieving F1 scores from 61. 38% to 63. 88% via local analysis on this channel. In comparison, their multi-trace approach resulted in F1 scores ranging from 64. 34% to 66. 78% on the different patient subsets. A total of 41 recordings from the CAP Sleep Database were analyzed in this research. Further details of the data and the methodology are shown in Table  1. Finally, the GTransU-CAP model was designed by You et al.
Materials and Methods In this section, the proposed methodology is described in four subsections. First, the selected database is described. Second, the process of symbolization of the EEG signals is detailed. Third, the vectorization tool is defined. Finally, the proposed classification models and the evaluation metrics are detailed. Figure  2  shows the flowchart corresponding to the methodology implemented for 1 dataset selection, 2 symbolic transformation, and 3 the vectorization process, includ-ing the Doc2Vec non-supervised training. The final step of the first part of the methodology is 4 to save the concatenated vectorized data. The classification steps are described in Figure  3 , where the proposed architectures for classification under two different validation strategies are detailed.
This methodology starts with 5 concatenated vectorized data loading, followed by 6 data splitting, and the 7 model's training and testing under the corresponding validation strategy. Finally, the 8 evaluation metrics are computed for 9 model comparisons or performance averaging. These processes are shown in parallel for simplicity, although hold-out validation was implemented before the K-fold cross-validation in order to be able to exclusively select and analyze the best-performing model. In contrast to the methodology described to train the Doc2Vec models (Figure  2 ), where unsupervised learning is implemented, supervised learning is used to train the classification models (Figure  3 ). For the first approach to the problem, the second largest group of patients was selected, i. e. , the 22 RBD recordings.
4. Support vector classifier with a radial basis kernel and a balanced approach to automatically modify the weights with an inverse proportion to the number of occurrences of each class in the training data; 5. Decision tree classifier with a maximum depth settled in 5; 6. Random forest classifier with a maximum depth settled in 5 and a maximum number of estimators fixed in 10; 7. Multi-layer perceptron classifier with a learning rate of 0. 1 and a maximum number of iterations settled in 1000; 8. AdaBoost classifier; 9. Gaussian naive Bayes classifier; 10. XGBoost classifier, with a maximum depth settled in 5, a maximum number of estimators fixed in 10, and a learning rate of 0. 1. This list of classifiers was validated using hold-out validation, with a training set using 80% of the data and the remaining 20% used for testing data to obtain a general scope of how the different classification models performed.
Results As stated before, the classification process was carried out using hold-out validation and stratified 10-fold cross-validation. Therefore, the results are presented and analyzed in the same order. However, the first relevant result of this research was found by exploring the selected data, which was the compound of channels Fp2-F4 and F4-C4 from 21 RBD recordings. Figure  5  shows a one-second segment of this data, whereas Table  3  shows the number of instances in the selected data per class, where the data is notoriously imbalanced. The most commonly used metric for describing the extent of imbalance of a dataset (i. e. , how imbalanced a dataset is) is the imbalance ratio (IR)  [40]. IR is defined by IR = N maj N min ; ( 5 ) where N maj is the number of examples in the majority class, and N min is the number of instances in the minority class.
Conclusions A novel methodology for CAP analysis based on data symbolization, vectorization, and classification is presented. First, a subset of 21 patients from the annotated CAP Sleep Database  [4]  was selected for this research. One-second segmentation was implemented on this data subset. Then, the symbolization process was performed using 1d-SAX, and the experimentation showed that it is a suitable form of transforming EEG signals into sequences of symbols. Further vectorization was carried out using two Doc2Vec models (one per selected channel) with the PV-DM algorithm. The resulting Doc2Vec-trained models were implemented to infer the vectors corresponding to the EEG segments of interest: those labeled as CAP1, CAP2, and CAP3 (according to the CAP A-phase type). The classification metrics on the 10 classic ML-implemented models have shown that increasing the dimensions of Doc2Vec does not necessarily improve the classification results.