dataesr/scientific-paragraphs-categorization huggingface dataset:
DCSS iteratively expands the model's generalization boundary by iteratively updating the credible data, thus minimizing the among-target differences and improving the model's adaptation ability. The proposed HUL minimizes the source-target differences from the detail and entire perspectives simultaneously by reducing the voxel-level and domain-level uncertainty concurrently. Experiments verified that: (1) DyDA improves the model's segmentation performance on the multiple-domain target site in different datasets and different segmentation tasks. The proposed DyDA is applicable and effective to different tasks and datasets for one-to-multiple medical image segmentation. (2) Dynamic and iterative division of the credible set can make the generalization boundary of the model gradually expand to cover multiple-domain data, thus reducing the among-target differences.
Acknowledgment This research was supported by the  Intergovernmental Cooperation Project of the National Key Research and Development Program of China  ( 2022YFE0116700 ),  CAAI-Huawei MindSpore Open Fund and Scientific Research Foundation of Graduate School of Southeast University  ( YBPY2139 ). We thank the  Big Data Computing Center of Southeast University  for providing the facility support on the numerical calculations in this paper.
This paper presents the results obtained by the MELODI team for the three tasks proposed within the DISRPT 2023 shared task on discourse: segmentation, connective identification, and relation classification. The competition involves corpora in various languages in several underlying frameworks, and proposes two tracks depending on the presence or not of annotations of sentence boundaries and syntactic information. For these three tasks, we rely on a transformer-based architecture, and investigate several optimizations of the models, including hyper-parameter search and layer freezing. For discourse relations, we also explore the use of adapters-a lightweight solution for model fine-tuning-and introduce relation mappings to partially deal with the label set explosion we are facing within the setting of the shared task in a multi-corpus perspective.
Several corpora have been annotated under each framework for different languages: however, even within the same framework, annotation guidelines and relation sets might be different for each corpus. The DISRPT shared task intends to provide a unified format for researchers to evaluate their systems against varied languages, domains, and frameworks. Three tasks were proposed: (1) discourse segmentation into EDUs, (2) identification of discourse connectives, and (3) classification of discourse relations based on attached units. The first two tasks are encoded with a BIO scheme over tokens, the latter corresponds to a multi-class classification between pairs of textual segments. The benchmark provided within DISRPT allows us to verify the robustness of our approach through 13 languages, 4 frameworks, and varied domains, including multi-party dialogues and speech transcriptions.
, 2017b). The first edition of the DISRPT shared task  (Zeldes et al. , 2019)  also revealed the same trend with performance above 95% for some corpora, but also issues with others such as the Spanish SCDT (82. 5% at best) or the Russian RRT (86. 2%). The bestperforming system in 2019  (Muller et al. , 2019)  was using a single model based on multilingual BERT for every corpus  (Devlin et al. , 2019) , while in the second edition  (Zeldes et al. , 2021) , the best system  (Gessler et al. , 2021)  relied on varied language models, either mono-or multilingual, associated to hand-crafted features: best overall performance was around 91. 5% on average, with a loss of about 2% when the sentences are not given. Connective identification was first seen as a word disambiguation task, where the goal was, starting with a list of candidates, to decide whether each occurrence is used in a discourse reading or not  (Pitler et al.
, 2008). It has been then recast as a sequence labeling one, where we need to decide whether a token starts, is within, or is outside a discourse connective  (Stepanov and Riccardi, 2016). As for segmentation, performance drops when existing systems are trained on new domains or languages  (Xue et al. , 2016; Scholman et al. , 2021) , but fewer studies investigated this task since implicit relations are more an issue for discourse parsing. The first two editions of DISRPT demonstrated rather high performance: between 92 -94 for the English and Turkish corpora, and 87 for the Chinese one, with only a small drop when sentences are not given. Discourse relations are the main object of study within the domain, with a specific focus on implicit ones since the connective is considered a very strong clue for guessing the relation  (Pitler et al.
Data The 2023 DISRPT shared task, including suprise datasets, provides 26 corpora for 13 languages and 4 theoretical frameworks: 9 correspond to the PDTB framework (thus connective and relations), the others are either RST (12), dependency (3) or SDRT (2) (thus segmentation and relations). Among these, 10 new corpora are introduced in the 2023 edition: 6 are released as surprise datasets, with one new language (Thaï), and out-of-domain (OOD) data for English (COVID-DTB and TED), Portuguese (CRPC and TED) and Turkish (TED). All statistics are given in Table  1. The largest corpora are the English PDTB (1, 992 training documents), dep SciDTB (492 documents), and RST DT (309 training documents), and, for SDRT, the French Annodis (64 documents). In total, 8 corpora have less than 100 documents and are thus considered very small.
, 2013) , 1 for Farsi  (Shahmohammadi et al. , 2021) , 1 for French  (Afantenos et al. , 2012) , 1 for Dutch  (Redeker et al. , 2012) , 1 for Russian  (Toldova et al. , 2017) , 1 for Turkish  (Zeyrek and Webber, 2008; Zeyrek and Kurfalı, 2017) , 1 for Italian  (Tonelli et al. , 2010; Riccardi et al. , 2016)  and 1 for Thai. In addition, OOD datasets come from the multilingual TED Discourse Bank with data for English, Portuguese and Turkish  (Zeyrek et al. , 2018 (Zeyrek et al. , , 2020)). 4 DisCut: segmentation and connectives 4. 1 DisCut: Model architecture Identifying EDU boundaries and connectives (Tasks 1 and 2) corresponds to different corpora: PDTB-based datasets have connectives annotated, but not segmentation, while the others have no connectives. However, they can be both modeled as sequence labeling tasks (only "Beginning" labels for segmentation, "Beginning" and also "Inside" for connectives, to take into account multi-words markers).
The input size is limited to 180. Our implementation relies on and extends the Jiant library 3  (Phang et al. , 2020). After evaluation on the dev set, we found that most models perform better with RoBERTa-large and with freezing the first 6 layers. Small improvements could be observed for some corpora with either the base version or other freezing values, but the increase was limited to less than 1. 2%, and in 3 https://jiant. info/ general less than. 5%, and we thus decided to favor one single model in order to make it easier to use, and better at generalizing to new data. Dealing with raw data: The DISRPT shared task proposes two tracks for tasks 1 and 2: you can either use data segmented into sentences and syntactically parsed (Treebanked) -either gold or obtained with Stanza -, or raw tokenized documents (Plain).
As the LMs have limitations on the size of their input, we can not give directly the documents as input: we thus decided to split the raw documents into sentences. However, having observed issues with Stanza segmentation, we tried alternatives: Ersatz  (Wicks and Post, 2021) and Trankit (Nguyen et al. , 2021) , and chose the latter based on better performance. Note that, with the evaluation being based on tokens, we had to realign tokens when the tool was modifying the tokenization. We were unable to obtain a correct sentence segmentation for the Italian ita. pdtb. luna, composed of speech transcripts, and thus cut every 120 tokens for this corpus. Dealing with surprise and OOD data: Dealing with the surprise Thai (tha. pdtb. tdtb) and English (eng. dep. covdtb) datasets were straightforward: since our model configuration is the same across all corpora, we retrain new models using the training data made available.
Experiments and results We present our results in Table  2  for segmentation and connective identification. Current comparison with 2021, considering only the corpora available in 2021, demonstrate general improvements for all tasks except connective for the Plain track were results are on par: for segmentation, the average on test sets for Treebanked is 91. 77% (vs 91. 48 for DiscoDisco 2021) and for Plain 91. 22% (vs 89. 79); for connective: 91. 81% (vs 91. 22) for Treebanked and 91. 05% (vs 91. 49) for Plain. Note that our approach uses a similar architecture with much simpler inputs (only tokens), and different optimizations. When comparing the reproduced results with the ones we produced, we observed a large variance between the scores, especially for small corpora, with for example a difference of about 2 to 5 points for the TEDm corpora, and about 2 to 3 points also for other small datasets such as the spa.
rst. sctb, the zho. rst. sctb, demonstrating the importance for future work to make multiple runs and indicate variance. Interested readers can find our own results on the test sets in Appendix A. As shown in Table  2 , compared to 2021, we observe a large drop in mean performance of about 10% for connective detection, for which many new corpora were added, including several OOD datasets making the task more challenging. For segmentation, the results for the two settings, Treebanked and Plain are in general very similar, except for the Chinese zho. rst. sctb and English eng. sdrt. stac for which the Treebanked setting is clearly better (+3 to 5%). On the other hand, we have an important improvement for the French corpus fra. sdrt. annodis (almost +3%) using our new segmented files (Plain): these results are in line with the bad performance observed for Stanza.
pdtb. cdtb (+1. 4), with, on the other hand, a loss of about 2% for the English PDTB, and an impressive drop of about 16% for Thai for which the model of sentence segmentation is probably faulty. Overall, the Plain setting would lead to average results on par with the treebanked ones for connective identification, without the Thai dataset (80. 54 on average for Plain against 80. 59 for Treebanked, without Thai). These results indicate that the good performance of the sentence segmenter is a key component of a well performing discourse segmenter or connective identifier. 5 DiscReT: Discourse Relation Tagging.
Dataset In order to stay faithful to the multilingual nature of the task, we decided to use all the datasets in parallel for training. Extensive earlier experiments with translations of the datasets to English, training with groups of corpora per language family, or training per annotation framework were not as successful or did not significantly outperform the accumulative approach. We aimed to reduce label space and maximize label coverage, i. e. not having a label that only exists in one corpus if it can be rewritten as a more general one. First, we lower-cased all labels in all datasets (but preserved our modifications, in order to reverse them for the final results in accordance with the Shared Task data). Second, we manually merged labels that were either spelling variants or simplified versions of existing labels.
For example, the label "qap" means "question-answer pair", which already exists as the label "question_answer_pair". Mean- while, the label "conjunction" is a simplified version of the label "expansion. conjunction" found in RST corpora in both forms, therefore by changing the label to its more verbose form, we are preserving its information and making the labels more uniform. However, we decided against the large-scale conversion of labels based on their meaning, e. g. merging the "conjunction" and "joint" labels. These conversions reduced the number of unique labels from 163 to 135; while the number was not significantly reduced, we wanted to make the results more interpretable without sacrificing important information. We present the implemented conversions in Table  3. We make use of the directional information of the relations, available in the datasets in the column "dir".
DiscReT: Model architectures We opted for transformer-based architectures for our experiments and tested several of them (mBERT, xml-RoBERTa, DistilBERT) in order to decide on which one to focus our research effort on. After preliminary tests, the multilingual BERT base cased model (mBERT)  (Devlin et al. , 2019)  was the most successful overall and included all the languages of the Shared Task in its pretrained version available from Huggingface. 4 As a "baseline" for our experiments, we trained an mBERT classifier built with PyTorch  (Paszke et al. , 2019) , without frozen layers, and trained for a maximum of 5 epochs. In order to inject additional information in the finetuning process of the classifier, without further changing the input data, we used adapters along-side our mBERT classifier.
Adapters  (Houlsby et al. , 2019)  are an alternative lightweight method to finetuning with equivalent good results on most NLP tasks. An adapter is a transformer architecture with layer-specific pretrained parameters Θ l which are frozen and a small set of new parameters Φ l (where l is the transformer layer). During finetuning, only the adapters' Φ l parameters are updated from the loss function L on dataset D (see Equation 1). This enables efficient parameter sharing between tasks, languages, etc. Φ * l ← arg min Φ l L(D; {Θ l , Φ l }) (1) We are using the tool AdapterHub  (Pfeiffer et al. , 2020)  which allows for easier finetuning and integration of adapters to transformer-based models. After several experiments, we observed that the finetuning process of an adapter is quite different than that of a model; the adapter set of parameters learns most effectively with more finetuning epochs than a normal model and the training process per epoch is longer.
Additionally, we experimented with freezing the parameters of certain layers for the models and the adapters, in order to determine the best model. We trained multiple mBERT adapters, out of which the most successful were: 1. mBERT adapter trained on the entire dataset for 15 epochs and with frozen layer 1 (A1) 2. mBERT adapter trained on the entire dataset for 15 epochs and with frozen layers 1-3 (A1-3).
This dramatically lowered the test results reproduced and published for the Shared Task. For clarity, we are reporting the Shared Task results from the organizers, but also include the results on the dev and test sets that were produced before converting the labels to their original in Tables  6  and 7  respectively. These results were calculated with scikit-learn  (Pedregosa et al. , 2011)  and the process of calculating them is transparent in our code. Our goal was to create a truly multilingual approach for discourse relation parsing. We did not aim to establish a new state-of-the-art, but to observe whether multilingual word embeddings can work in synergy (to learn common labels) and specialize at the same time (to learn corpus-unique labels). We also deliberately focused and submitted a combination of three models, instead of proposing the best model for each dataset, thus sacrificing performance for reproducibility.
During our experiments, there were other combinations of adapters and models with frozen layers that yielded slightly better results on specific corpora, however, the training times for multiple models would be problematic for a Shared Task entry. Given that our results are not much worse than approaches with a combination of monolingual models and independent training, it is possible to derive benefits from joint training and evaluating multiple languages. Our multilingual models showed strengths (e. g. in the spa. rst. rstsb dataset) and weaknesses (e. g. in English, Turkish and Chinese datasets) that cannot be pinpointed directly to a specific framework, the size of the corpus, or the size of the specific language data, and will need to be further explored. Our submission was marred by implementation issues, but we are hopeful that in future work we will tackle these issues and implement improvements on our multilingual approach.
Conclusion In this paper, we presented our submissions for the three tasks of the DISRPT Shared Task. Our main goals were to rely on only a few architectures variants for generality, and experiment with parameter efficient methods. For Tasks 1-2, we employed multi-task, multi-corpora approaches; however, at this stage of our research our results are not opti-  mal. In future work, we aim to further explore this strategy, as it seems promising for lower-resource languages. Additionally, we are interested in approaches beyond the scope of this campaign, such as domain transfer. Furthermore, it was possible to perform segmentation and connective detection on datasets without training data, as shown by the surprise TEDm test sets. It would be interesting to examine whether the DISRPT framework could be transferred to new languages, for which there are no training data for segmentation or connective detection, such as the rest of the TEDm corpus.
As for Task 3, our focus was on a unified, purely multilingual approach with parameter optimization, as well as dataset preprocessing for unification. Even though we faced problems on the Shared Task submission results, our approach showed promising results compared to language-specific models. In parenthesis is the number of epochs for which the model was trained. 'DiscoDisco' was the best-performing model of DISRPT 2021  (Gessler et al. , 2021)  and 'Diff. ' is the comparison with our models. MEAN (all) provides the mean for the currently available datasets, while MEAN (2021) averages only DISRPT 2021's corpora.
Acknowledgements This work is supported by the  AnDiaMO  project ( ANR-21-CE23-0020 ). This work was partially supported by the  ANR  ( ANR-19-PI3A-0004 ) through the  AI Interdisciplinary Institute ,  ANITI , as a part of  France's "Investing for the Future -PIA3" program. This work is also partially supported by the  SLANT  project ( ANR-19-CE23-0022 ). Chloé Braud  and  Philippe Muller  are part of the programme DesCartes and are also supported by the  National Research Foundation, Prime Minister's Office, Singapore  under its  Campus for Research Excellence and Technological Enterprise (CREATE)   programme. The work was also supported by the  ANR  grant  SUMM-RE  ( ANR-20-CE23-0017 ).
In this analysis, we first compute shot gathers from multichannel active-source data by applying cross-coherence analysis. We then stack the cross-coherence gathers from several shots at a single observation position. By applying CWT-MASW to the cross-coherence gathers, we estimate phase velocity dispersion curves. The CWT-MASW can be divided into two steps. In the first step, group velocity dispersion curves are estimated from stacked amplitude of shot gathers in time-group velocity domain with CWT. In the second step, phase velocity dispersion curves are estimated by MASW only using phase at arrival times of the group waves estimated in the first step. The second step allows us to give a large weight on localized surface wave signals if group velocities of surface waves are extracted in the first step.
Depth profile of S-wave velocity We deployed the LASP on a site at a sand beach, where the poorly sorted sediment is a fair proxy of lunar regolith. To ensure optimal conditions, we conducted the experiment on the farthest part of the beach from the sea, where the sand is relatively less saturated with fluid. We generated chirp signals that combined up-sweep and down-sweep in frequency and recorded the waveforms with the receivers on the LASP. We identified clear surface waves on the cross-coherence gather (Fig. 2a ). Because of short-offset data and limited frequency range of the source system, it was difficult to identify clear body wave on the crosscoherence gather. However, the experiments with longer offset data show clear body waves. We applied the CWT-MASW approach for the surface wave to estimate stable dispersion curves (Fig.
Discussion and summary Our results demonstrate that the LASP enables as to image the shallow subsurface with high accuracy (Fig. 2d ). Our method is accurate enough to investigate the distribution of ice in extraterrestrial regolith. To evaluate the sensitivity of S-wave velocity for the freezing of ice, we conducted the laboratory experiment using regolith simulant (see Appendix). Because experiments show that S-wave velocity varies by ~40 m/s with the presence of 0. 5 wt% ice (Fig. A2 ), the accuracy of velocities achievable with LASP data (~0. 5 m/s in Fig. 2d ) is good enough to identify regions with 0. 5 wt% ice saturation as an S-wave velocity anomaly. Furthermore, with the LASP we can detect 3D subsurface structures, including lithological boundaries (Fig. 7 ). This information could be useful for construction of base camps as well as telescope deployments on the Moon.
Acknowledgements This study was supported through the  Japan Society for the Promotion of Science (KAKENHI  grant  JP20H01997 ;  JP21H05202 ;  JP22H05108 ), the  Institute of Space and Astronautical Science/JAXA , and Project  of technological innovation for Construction on Space  field of the  Ministry of Land, Infrastructure, Transport and Tourism. We thank  Sean Gulick  (UTIG) and another anonymous reviewer for constructive comments. We also thank  K. Kitamura  ( Kyushu University ) for providing the equipment for part of our seismic velocity measurements, and  Yoshiaki Ishihara  (JAXA) and  Yuzuru Karouji  ( JAXA ) for the fruitful discussion.
Assessment of patient survival could improve decision-making in a clinical environment. Additionally, certain treatment options are allowed to be used only in cases of poor prognosis. Clinical and biochemical markers, such as stage and grade of tumor, are often used in the assessment of ccRCC prognosis  [19]. Recently, we have shown that, for most cancers, survival prediction can be improved by incorporating clinical and transcriptomic data  [20]. Additionally, there is an increasing interest in how ECM-related genes could be used to assess the survival of ccRCC patients  [21] [22] [23] [24]. To the best of our knowledge, COL7A1 expression in ccRCC was not investigated as an independent prognostic biomarker in this disease, and this is the first ccRCC study using five independent public datasets.
Datasets and Processing The cancer genome atlas (TCGA) is a cancer genomics program that has characterized over 20,000 tumoral and peritumoral tissue samples from 33 different cancers, including ccRCC (with the acronym KIRC). We obtained the transcriptome profiling data of primary tumor tissue and the corresponding clinical information of the TCGA:KIRC cohort using the "TCGAbiolinks" R package (v2. 62) [25]. As per a recent suggestion  [26] , we downloaded harmonized transcriptomic data, which was already transcripts per million (TPM) normalized. To decrease computational time, we retained only the protein-coding genes with total expression across samples greater than 10 TPM. Next, we applied the log2(TPM + 1) transformation to the data. To deal with sample multiplicity in GDC data (more than one sample per patient), we only analyzed samples that could be found in GDC "FireBrowse" (http://firebrowse.