dataesr/scientific-paragraphs-categorization huggingface dataset:
Then some variant models of BERT in the field of biomedicine are proposed, and great success has been achieved in biomedical natural language processing tasks. They are all pre-trained on large domain corpora. For example, BioBERT  [19]  is initialized with weights from the original BERT and then is pre-trained on biomedical domain corpora (PubMed abstracts and PMC full-text articles) for 23 days on eight NVIDIA V100 GPUs  [19]. Blue_BERT (NCBI BERT, Biomedical Language Understanding Evaluation)  [20] , another popular BERT-based model in the biomedical domain, is pre-trained on PubMed abstracts and MIMIC-III clinical notes. Based on the support of the domain knowledge source (Unified Medical Language System), this paper applies domain BERT to the constructed term graph and achieves good disambiguation results.
III. WSD BASED ON WEIGHTED CO-OCCURRENCE BIO-TERM GRAPH (WCOTG) A. SYSTEM DESCRIPTION Our system relies on a data model of weighted co-occurrence bio-term graph and exploits a modified Personalized PageRank (PPR) to generate disambiguation in the biomedical domain. The complete system is shown in Fig. 1. Part 1 illustrates the steps for building a weighted co-occurrence bio-term graph. Each bio-document in the original set is transformed into biomedical terms through automatic term extraction, and this work is based on a dictionary, e. g. , the dictionary auto-generated from the file MRCONSO. RRF 2 in the Unified Medical Language System (UMLS) Knowledge Sources. Then each term set forms a fully connected graph. The terms extracted from the bio-document eventually become the nodes of the graph, and the co-occurrence relations between terms, i.
We have discarded those instances with the None category, so finally, the test collection contains 3, 983 instances and 49 ambiguous terms out of the 50s since the instances of the ambiguous term ''association'' have been assigned entirely to None. Another test dataset is named ''MSH-WSD Test Collection''. It consists of 203 highly frequent ambiguous terms and contains both biomedical terms and abbreviations. 37, 888 ambiguity instances are randomly selected from the 2010 MEDLINE citations. Each instance of the ambiguous word has a correct sense annotated by human evaluators. Each ambiguous case contains approximately 187 instances and has 2. 08 possible senses. There is no None category marked in this test collection. In this work, we present an unsupervised system that does not need any annotations, however, we need to acquire data to build the co-occurrence term graph.
B. ACRONYM CORPUS We also validate the proposed method on the acronym test dataset. Stevenson and Guo  [38]  provided an acronym corpus that has been successfully used in WSD research  [5] ,  [39] ,  [40]. However, we failed to successfully download this corpus, so we built new acronym corpora containing 22875 abstracts downloaded from Medline. Each of these abstracts contains an ambiguous acronym from the set of 18 acronyms originally developed in  [41]  and is widely used in previous studies. Each acronym, used as an ambiguous target term in evaluation, consists of 3 letters, and it is associated with between 2 and 4 extended forms which are considered candidate senses. We create 18 corpora, and each corpus is corresponding to a target acronym. The specific creation process is similar to the process of creating the corpus for building graphs introduced in Section IV-A.
The difference is that extended forms, i. e. , candidate senses, are gotten by querying each item in the MRCONSO. RRF file of UMLS Metathesaurus, instead of from the NLM-WSD or MSH-WSD Test Collection. For example, as to the acronym ''ANA'', two extended forms ''American Nurses Association'' and ''Antinuclear Antibody'' have the same CUI as it in the MRCONSO. RRF file. So they are extracted from the MRCONSO. RRF file as the candidate senses for the acronym ''ANA''. And then a search query is made in Medline based on each candidate sense. We download 600 (Or the maximum number that has been searched) PubMed format files for each candidate sense and convert them to 600 files that only contain abstracts. Then we split these abstract files into two parts. One is used to form the test datasets, and the other is used as the corpus for graph building.
In partitioning, the data used to create the graph will never be included in any test datasets. Most of the test datasets are evenly distributed, i. e. , there is nearly the same number of test instances corresponding to each candidate sense, except the test dataset for the acronyms ''CMV'', ''DIP'', ''LAM'' and ''MAC'', due to an insufficient number of instances in the original corpus. Four test datasets are created and referred to as T100, T150, T200, and T300. Table  1  provides a resume of the acronym datasets used for the evaluation. It can be seen, the number of senses per term in T100 and T200 is fixed. That is, the first two datasets T100 and T150 both have fixed numbers of senses for each ambiguous word, 2 for T100 and 3 for T150 respectively. All 18 acronyms are present in T100. For an insufficient number of their extended forms, the acronyms ''ANA'', ''BPD'', ''EMG'', and ''RSV'' are not present in the data set T150 whose sense length is 3.
The last two datasets T200 and T300 are mixed-length datasets, and each acronym has between 2 and 4 extended forms. These two datasets correspond to the datasets A200 and A300 respectively, that is, they have the same acronyms. Table  1  compares the databases of the T series and A series in terms of sense length (average senses), the number of abstracts for graph building, and Min/Max senses per term, etc. We can observe from Table  1 , that the number of abstracts for building graphs is much smaller than that of the Bio-Concept-Graph method (BCG)  [5]  introduced in Section II. And the average number of possible senses in the T serials of acronym datasets is higher than that in the A serials of datasets, except T100. In our experiment on acronym disambiguation, it is found that the accuracy of disambiguation on the acronyms with two senses is much higher than that with three senses.
B. EVALUATION ON THE MSH-WSD TEST COLLECTION OF 203 AMBIGUITIES We also evaluate our method on the MSH-WSD Test Collection (203 ambiguous words) and compare experimental results with the work of Mao and Wah  [6] , which applies BERT variant models for WSD in the unsupervised biomedical domain and using the same test dataset. As we can observe in Table  5 , over the same test collection, without extra training, the BioBERT_L method outperforms the best BlueBERT_LE-based method that enhanced the BlueBERT_Large model with additional training by the concept definitions in the UMLS  [6]. While the COP-based WCOTG obtain similar disambiguation results to it. We can also see that the accuracy of WCOTG (weighted method) based on two kinds of weights currently tried both outperform the UWCOTG (unweighted method).
C. EVALUATION ON ACRONYM TEST DATASETS On the built acronym test datasets, we verify the difference in disambiguation accuracy between the term-graph method and concept-graph one both based on the co-occurrence graph ideas. Table  7  shows the experimental results of the WCOTG and UWCOTG methods based on the co-occurrence term graph on the T serial test datasets against the Bio-Concept-Graph (BCG)  [5]  method based on the co-occurrence concept graph on the parallel A serials test datasets. It can be seen from Table  7  that the WCOTG (BioBert-based) outperforms others over all test datasets listed in the table. Table  8  shows a word-by-word comparison result of the WCOTG and Bio-Concept-Graph (BCG) methods. As we can observe in the table, over the test dataset with 100 instances, the WCOTG offers all the best results (including 2 equal cases), and the average accuracy of WCOTG reaches 0.
9939. Over the test dataset with 200 instances, the WCOTG offers almost all the best results, except in the case of the acronym ''PEG''. And over the test dataset with 300 instances, the WCOTG offers the best result in 8 out of 14 cases while the BCG offers 6, and the results are basically flat. However, the total number (TN_graph) of abstracts used for building graphs varies greatly, with a maximum number of 21075 for the WCOTG-based system and 50143 for the Bio-Concept-Graph based one. In other words, our method only uses almost half the composition level relative to the Bio-Concept-Graph based method. Table  9  shows the concrete performance of the UWCOTG and WCOTG methods -both based on bio-term graphs -over the T serial acronym test datasets, and Table  10  shows a word-by-word comparison of results.
As we can see from Table  10 , over the T100 dataset with 2 senses per acronym, the WCOTG offers the best result in 17 out of 18 cases, including 12 equal cases with the UWCOTG. Over the T150 dataset with 3 senses per acronym, the WCOTG offers all the best results, only including 1 equal case. The efficiency gap between the UWCOTG and WCOTG systems is only 0. 7 ((0. 9939-0. 9867) * 100%) percentage points over the T100 with 2 senses per acronym. However, over the T150 with 3 senses per acronym, the efficiency gap between the two becomes 12. 3 ((0. 9602-0. 8372) * 100%) percentage points. The WCOTG outperforms the UWCOTG in 15 results over the T200 dataset, and 12 results over the T300 dataset. In all, the WCOTG system performs much better than the UWCOTG system on the test dataset with 2 more senses per acronym.
D. COMPARATION OTHER WORKS BASED ON BERT MODELS As illustrated above, our system uses the Unified Medical Language System (UMLS) Knowledge Sources in the biomedical field for building term graphs and has completed the experimental comparison on the NLM-WSD, MSH-WSD, and acronym test collections in the biomedical field. In recent years, there have been some reports in the general domain that BERT models have been well applied in WSD tasks, but their test sets usually come from the general domain. In this part, we summarize and compare the efficiency of some BERT-based WSD systems both in the general domain and biomedical domains. Table  11  shows the comparative results. Wherein, in the general domain, the GlossBert  [25]  fine-tunes the pre-trained BERT model on the training corpus. The BEM  [26]  presents a bi-encoder model built on top of BERT that is designed  to improve performance on rare and zero-shot senses.
VI. CONCLUSION AND FUTURE WORK This paper describes the application of an unsupervised method based on weighted co-occurrence term graph for performing WSD in the biomedical domain. We build graphs from terms extracted from biomedical abstracts directly, instead of concepts gotten through the mapping tools. This avoids the bottleneck of mapping terms to concepts when transforming polysemous terms in biomedical documents into concepts. The most important contributions of this paper are that we add weights to the links of the built bio-term graphs, and take them as important factors in the disambiguation algorithm. Furthermore, a sub-term graph with a relatively small amount of data is extracted from the general one, to reduce the operation scale of the disambiguation algorithm. And the PPR algorithm which is normally applied over the unweighted directed graph is modified for the WSD task over our built weighted undirected term graph.
The paper provides comparative evaluations of similar previous methods and several WSD methods over the NLM-WSD, MSH-WSD test datasets, and an acronym test dataset. The results show that the proposed unsupervised WCOTG method outperforms the concept-graph based one on a very similar corpus scale, and outperforms some unsupervised ones addressing the same problem. Comparative experiments of weighted and non-weighted bio-term graphs validate the positive effect of links' weight on disambiguation efficiency. Further statistical experiments on the relationship between system accuracy, the numbers of biomedical abstracts in the corpus, and the corresponding extracted terms suggest an excellent minimum corpus scale when resources are limited. Although our method achieved good overall disambiguation efficiency on the three datasets tested, there were also some failed cases in the experiment.
We call this method the contrast grid. Finally, we analyze the error budget caused by non-Gaussian noise w. r. t. the detection limits in Section 7. The code of the metrics presented in this paper is public available as a python package called applefy on GitHub https://github. com/markusbonse/applefy. A detailed documentation page on how to use the package, including many tutorials, can be found at ReadtheDocs https://applefy. readthedocs. io/.
What is a Detection? The data postprocessing routine combines the sequence of individual observations taken over the course of one night into a single image. In this residual image potential planet candidates appear as bright spots. A priori, we do not know if a bright spot is actually a planet, and the decision of whether it is bright enough to be counted as a detection is always a balancing of the risk that it is just part of the noise. In order to quantify this risk, we use hypothesis testing. The classical approach based on the t-test (for a general background on hypothesis testing and the t-test see Chapter 9 of Larsen & Marx 2012) was introduced by  Mawet et al. (2014)  and is illustrated in Figure  1. The test procedure can be split into three main steps. In the first step, we extract values for the noise and the potential planet.
Throughout the rest of this paper we compute our results based on an L¢-data set of β Pictoris taken with the AGPM coronagraph  (Delacroix et al. 2013)  and NACO  (Rousset et al. 2003)  at the Very Large Telescope (VLT). The data set is the same as the one used in  Mawet et al. (2014). The planet β Pictoris b was removed by insertion of a negative fake planet. More details on the data can be found in  Absil et al. (2013). Our data preprocessing routine uses the python library PynPoint given in  Stolker et al. (2019). The final stack of preprocessed frames consists of 29,681 images.
We further note that in reality, the spatial correlations can deviate from the shape of the PSF e. g. , due to data postprocessing. An empirical analysis on this topic is given in Appendix E. In the background-dominated regime the use of apertures might be preferable over the use of spaced pixels. Since, the photon noise occurs on a pixel-by-pixel basis it is not problematic with respect to independence. But, the signal estimate based on the brightest pixel is prone to statistical fluctuations and biases such as hot pixels. The choice whether apertures or spaced pixel are used should be taken on a caseby-case basis, which is why our python package applefy provides an implementation of both. Future work should further investigate more sophisticated alternatives to the use of spaced pixel including explicit models of the pixel-to-pixel dependencies (compare, e.
No matter how close  m and ŝ (or b) are to the true values of  m and σ (or b), we always obtain the exact FPF. The estimation of the FPF becomes a simple lookup. We can calculate p(T * = t|H 0 ) once and reuse it for future calculations, which drastically reduces the computation time. The distribution of p(T * = t|H 0 ) depends on the sample size n, which is why we have to compute separate lookups for different separations from the star. In order to achieve accurate results for low FPFs, the number of bootstrap samples B has to be large. For example, if we aim for a FPF of 2. 87 × 10 -7 (  5s ), we expect to observe one false positive every B = 1/2. 87 × 10 -7 = 3. 48 × 10 6 iterations. To constrain p(T * = t|H 0 ) with sufficient accuracy, we need about B = 10 8 bootstrap samples. In our python package applefy we provide precomputed lookups within 1-20 λ/D for B = 10 8 and interfaces which can be used to extend the code to other noise distributions.
How to Quantify Detections With Parametric Bootstrapping? Current standards for contrast quantification in HCI (compare Section 2) address two main research questions: first, the detection problem, i. e. , whether a potential candidate is real (Q1), and second, the quantification of the detection limits (Q2). The following two subsections give a detailed recipe of how parametric bootstrapping can be used to address these two questions. An implementation is given in our python package applefy.
In extreme cases an increase in planet brightness does not lead to a stronger signal in the residual. A detailed discussion of the effect is given in Appendix B. The number of PCA components used during data postprocessing affects the achieved contrast. While a higher number of PCA components leads to a stronger reduction of the noise, it also causes more loss of planetary signal. For a given data set fewer components give higher contrast at close separations and more components provide better results for large separations. We recommend computing contrast curves for a range of PCA components and report the overall best  (Xuan et al. 2018). Our python package applefy provides the code needed to automate and parallelize these computations. Under additional assumptions and for some data postprocessing algorithms it is possible to compute the contrast curve analytically.
Alternatively, we can use nonparametric methods such as nonparametric bootstrapping to estimate the statistics directly from the data. Future work should further investigate the origin of non-Gaussian noise and develop new methods to account for the spatial dependencies of speckle noise. main analyses and wrote the manuscript. He further programmed the publicly available the python package applefy and wrote the documentation page on ReadTheDocs. E. O. G. proposed the initial idea to explore parametric bootstrapping and pivoting to address the detection problem. T. D. G. contributed to the programming of applefy. T. D. G. and G. C. helped to debug the alpha version of the code. All authors discussed the results and commented on the manuscript. G. C. thanks the Swiss National Science Foundation for financial support under grant number P500PT 206785.
Acknowledgments We thank the anonymous referee for a critical and constructive review of the original manuscript which helped improve the quality of the paper significantly. This work was supported by an  ETH Zurich   Research Grant. M. J. B. and S. P. Q. gratefully acknowledge the financial support from  ETH Zurich. Parts of this work has been carried out within the framework of the  National Centre of Competence in Research  PlanetS supported by the  Swiss National Science Foundation (SNSF)  under grants  51NF40_182901  and  51NF40_205606. Parts of this work were supported by the  SNSF  via grant No. 200020_200300. S. P. Q. , E. O. G. , F. A. D. , and J. H. acknowledge the financial support of the  SNSF. O. A. acknowledges funding from  F. R. S. -FNRS , and from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No.
, 2022 ). An interesting recent development is the use of Neural Radiance Fields (NeRF) to render a 3D scene of objects  (Mildenhall et al. , 2020). NeRF attempts to recreate radiance fields in the form or neural networks; it is fundamentally different from pre-existing notions of 3D models familiar to photogrammetry users. NeRF, in and on itself, represents neither a 3D mesh nor voxels but rather a density function describing the transparency or opaqueness of a certain object seen from a specific point of view (namely the images). As such, NeRF was not originally developed for 3D reconstruction but rather 3D rendering, i. e. the creation of novel points of view from existing base dataset generated by a trained neural network. However, several computer graphics techniques may be utilised to convert NeRF into either a point cloud or a 3D mesh, for example by using the classical marching cubes algorithm  (Lorensen & Cline, 1987).
The images were then oriented using Agisoft Metashape and then fed into Nerfstudio (https://docs. nerf. studio/, accessed 11 April 2023). Nerfstudio is a simplified platform and interface to several implementations of NeRF; in this paper the Nerfacto  (Tancik et al. , 2022)  variant was used for the experiments. After training and recreation of the 3D scene by Nerfstudio, a marching cubes algorithm was implemented to convert the radiance fields into a 3D point cloud. Conversely, the oriented images were thereafter used to also generate a dense point cloud using Metashape using the "High" preset. A comparison between the two point clouds was then performed using the software CloudCompare (https://www. danielgm. net/cc/ accessed 11 April 2023). In this study, two heritage objects were recorded and compared.
These two datasets include: dataset A comprising 24 images of the Reformers' Wall in the city of Geneva (Switzerland), and dataset B made of 85 images of the main entrance to the Bern Minster (Bern, Switzerland) with a Gothic tympanum depicting the Last Judgement. Dataset A is a simpler case reminiscent of the case of classical stereopairs set along one axis with overall also simpler object details, while dataset B presented a more complex object due to the various sculptures and ornate upper part of the tympanum. Both datasets were processed in Agisoft Metashape and then their point clouds compared to the ones generated by Nerfacto, with future test also planned for implementation in Instant-NGP. Theoretical ground sampling distances (GSD) of 1. 3 mm for dataset A and between 1 and 3. 5 mm for dataset B were calculated to manage the expected result for the geometric quality.
RESULTS AND DISCUSSIONS Results showed that Nerfacto generated 3D renders in seconds, with training process totalling longer but achieving a stable result after a few minutes. Point cloud conversion was near instantaneous, but as can be seen in Table  1 , noise is still prominent in both datasets as evidenced by high values of standard deviation when compared to the reference Metashape point cloud. Three quality parameters will be considered for each individual dataset. First, to check the geometric quality the value of mean error (x ̅ ) and standard deviation (σ) from an M3C2  (Lague et al. , 2013)  comparison performed in CloudCompare were used to assess Nerfacto's results, in terms of systematic error and presence of noise respectively. As both point clouds originate from the same network of oriented images, the value of x ̅ is expected to be close to zero.
The first and second parameters are shown in Table  1 , while the third parameter is described in Table  2. For dataset A, Nerfacto generated around 1. 2 million points with notable upper parts lacking in point density. This may be due to the fact that the images were taken from a lower point of view and oriented upwards; thus, the upper parts of the wall present the farthest distance from the camera. It is therefore interesting to note that object-to-sensor distance may play a role in determining the quality of the point cloud. It managed nevertheless to attain a completeness score of 92. 2%. In the third row of Table  1 , missing and incomplete parts of the object are coloured grey. In terms of geometric accuracy, it yielded a mean error of 2 mm which is consistent with the initial hypothesis.
It is however more interesting to observe the value of the standard deviation, of which a value of 3. 5 cm was registered. This is more than 25 times higher than the theoretical GSD. Furthermore, considering a 2. 7σ tolerance as is usual in surveying applications, it is about 10 times higher than the tolerated threshold. This is also evident visually, as can be seen in Table  1. A similar observation can be seen in dataset B, where Nerfacto managed to generate 7. 1 million points. The mean error is again consistent with the initial assumption, but the standard deviation reached 5 cm, which is around 15 times larger than the GSD. Within dataset B, missing parts can also be seen where they are expected to be, namely image blind spots and higher parts where the object to sensor distance is much higher.