dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, we no longer have semantic classes, e. g. swimming pools, which are absent in some of the Zurich test images. Table IV reports the evaluation results of building extraction on the Vaihingen dataset. On this dataset, the highest scores among the APs were achieved with the Vol-CPWS-AP, which outperformed one of the best methods in the literature, namely AP, by 0. 62%, 2. 96%, 5. 13%, 4. 39% and 1. 50% in terms of OA, precision, recall, F1 and mIOU, respectively. Regarding the use of prior knowledge on Watershed-APs, both Area-CPWS-AP and Area-FPWS-AP (resp. Vol-CPWS-AP and Vol-FPWS-AP) outperformed Area-WS-AP (resp. Vol-WS-AP) with respect to all metrics, except for precision, which validates the interests of semantic prior knowledge in the context of building extraction. The largest improvement was observed for the Watershed-AP computed with volume, with the Vol-CPWS-AP being 1.
23%, 3. 27%, 3. 96%, 3. 75% and 3. 95% better than Vol-WS-AP with respect to OA, precision, recall, F1 and mIOU scores, respectively. All three FPWS-AP presented very similar results and outperformed their respective WS-AP counterparts by more than 4%, 2% and 4% respectively in terms of recall, F1 and mIOU scores. Classification results on one image of the Vaihingen dataset are illustrated in Fig. 10. The true positives, false negatives and false positives are represented in white, red and cyan, respectively. On this image, we can observe that the area and volume Watershed-APs produce less noisy classification results when compared to the other methods. In particular, the Vol-CPWS-AP presents a higher precision than all other methods. Table XI presents the evaluation of building extraction on the Zurich dataset.
With respect to the baseline NIR+RGB, all APs (except for AP-minT) provided better scores for most metrics. For each metric, the highest score was achieved by one of the Watershed-APs, with Area-FPWS-AP giving the highest OA, F1 and mIOU scores among all tested methods. In general, FPWS-APs led to more pixels being classified as belonging to the building class, increasing the number of true and false positives, as attested by the lower precision and In conclusion, Watershed-APs computed with the help of semantic prior knowledge showed its usefulness in both landcover classification and building extraction on multispectral data. On both tested datasets, the highest scores were achieved by one of the Watershed-APs, whether it was with CPWS-APs or FPWS-APs. Based on our experiments, the effectiveness of considering semantic prior knowledge either during the construction phase of hierarchical watersheds or during the filtering step of Watershed-APs depends on the task and on the dataset under study.
VII. CONCLUSION This article proposed the Watershed-AP as an extension of AP to hierarchical watersheds computed from (edge) weighted graphs. Besides, the relevance of using semantic prior knowledge was considered in the construction and filtering of such hierarchies. We evaluated and validated our approach on the pixel classification and building extraction of two multispectral remote sensing images, which showed the potential of hierarchical watersheds in this field. On both datasets, the best results were achieved by Watershed-APs computed or filtered with prior knowledge from training pixels. As future work, we will to explore the versatility of hierarchical watersheds by considering other methods to obtain the.
line 5, C(x) is thus computed in O(m × d), where d is the maximal depth of each tree. Considering that the number n of semantic classes is a small constant in most datasets, the for loops of lines 4 -9 are executed in time O(|I| × m × d). The graph G can be constructed in time O(|I|) given that each vertex in V corresponds to a pixel in I and that the number of edges increases linearly with the number of vertices in a 4 or 8connected graphs. That being said, the three for loops in lines 14 -21 are executed in time O(|I|). Finally, the hierarchical watershed H can be obtained in time O(|I|log|I|), as stated in [29]. Therefore, the overall time complexity of Algorithm 1 is O(m×t×log(t)+|I|×m×d+|I|log|I|) if we consider the learning step of the classifier C. However, since C is trained only once, we can simplify it to O(|I| × m × d + |I|log|I|), which is a function of the number m of trees of C, their depths and the dimensions of the image I.
Given a dataset I and its training set composed of n classes, we propose the following pipeline: (i) Computation of a hierarchical watershed H of I; (ii) Training of a classifier using the training set of I and computation of per-pixel class probabilities (p 1 ,. , p n ) for all pixels of I; (iii) For each probability map p i , computation of a list L i = (p 1 i ,. , p k i ) of k binary thresholded versions of p i such that, in each map p m i , white or one valued pixels are the ones whose class probabilities are larger than a given threshold; (iv) Filtering out the nodes of regions of H which only contain black or zero-valued pixels; (v) Reconstruction of an image from each of the filtered version of H computed in the previous step.
Ratiometric fluorescence imaging We acquired fluorescence images of the heart of 3 dpf Tg(myl7:ECFP-16aa-EYFP) larvae, and 3 and 5 dpf Tg(myl7:Twitch-4) larvae with a wide-field fluorescence microscope (DMIRE-2, Leica Microsystems, Germany) equipped with a sCMOS camera (2048 × 2048 pixels, ORCA-Flash 4. 0, Hamamatsu Photonics, Japan), controlled by the software Aquacosmos 2. 6 (Hamamatsu Photonics, Japan). The image acquisition rate was 50 Hz (20 ms integration per image) during 5-10 s; some larvae were imaged at 100 Hz. Larvae under the microscope stage were kept at 28ºC in a chamber incubator (PeCon GmbH, Germany). To excite the fluorophores, light from a LED source (Lambda TLED+, Sutter Instrument, CA, USA) was applied continuously for 5-10 s using a 440AF21 nm bandpass filter (Chroma, VT, USA) and a beamsplitter 455DLRP (Omega Optical, VT, USA).
Image processing and data analysis Ratiometric images and ratio data were processed and analyzed with an analysis program (Ratioscope) written in the IGOR Pro environment (WaveMetrics, OR, USA). We corrected for pixel shift between FRET and donor images. The ratio FRET image/donor image was calculated pixel-by-pixel for each time point. Regions of interest (ROI) were drawn over the atrium and the ventricle walls in diastole. The ratio value for a ROI was calculated by averaging all the pixels' values weighted by the average intensity of donor and FRET channels  [34, 35]. Since the ratio for pixels close to background can reach infinite values, pixels with values smaller than the minimum displayed ratio/4 or larger than the maximum displayed ratio×4 were clipped. If necessary, the Savitzky-Golay smoothing filter was applied to reduce the noise in the raw ratio traces.
Several kinetic parameters were automatically calculated from the ratio traces: diastolic ratio (the lowest ratio in the cardiac cycle), systolic ratio (the highest ratio in the cardiac cycle), ratio amplitude (ΔR, systolic minus diastolic ratio), heart rate (HR, in bpm), rise time (time from 10 to 90% of systolic Ca 2+ rise), decay time (time from 90 to 10% of diastolic Ca 2+ decay), rise slope (ΔR from 10 to 90% value, divided by the rise time) and decay slope (ΔR from 90 to 10% value, divided by the decay time) (Figure  1F ). The Ca 2+ transients and the points of interest indicated in Figure  1F  were detected automatically using the edge detection functions provided in the Igor Pro (WaveMetrics) environment and implemented in a custom-made analysis module ("Pulse" module in the "Ratioscope" package) (Figure  S1 ).
Data shown for each larva represent the average of all the cardiac cycles in 5 or 10 s of continuous recording. To monitor contractile parameters in Tg(myl7:Twitch-4), we wrote an analysis program in IGOR Pro that automatically detects contraction events (Figure  S1 ) and records changes in the ventricular diameter during the cardiac cycle (Figure  4A ). A line drawn through the ventricle in the fluorescence images tracks the movement of the wall and the displacement of the ventricular walls is represented in a kymogram. The FS was calculated as the difference between the end-diastolic and end-systolic diameters, divided by the end-diastolic diameter. The external wall was used to measure the ventricular diameter since the inner wall did not provide sufficient contrast. The program used to calculate all these measurements in the Igor Pro environment is publicly available as "Ratioscope 8.
Statistics Statistical analysis was done with GraphPad Prism 8 (Graphpad Software, CA, USA) and Igor Pro (WaveMetrics, OR, USA). The number of independent experiments (experimental days, N) and the number of larvae (n) are indicated in each figure. The Shapiro-Wilk test was used to test for normality. Differences between two groups were analyzed using the unpaired Student's t-test for parametric data or the Mann-Whitney test for non-parametric data, as indicated. Comparisons between experimental groups were analyzed by one-way repeated measures ANOVA with Dunnett's multiple comparisons post-test for parametric data or the Friedman test with Dunn's multiple comparisons post-test for non-parametric data, as indicated. Data are shown as the mean ± SD. A p < 0. 05 was considered statistically significant and significances are indicated as * for p < 0.
Effect of ryanodine and caffeine on cardiac Ca 2+ levels and ventricular shortening of zebrafish larvae We examined the effects of compounds acting on RyR on Ca 2+ levels and heart shortening. First, we tested the response of 3 and 5 dpf Tg(myl7:Twitch-4) larvae to the inhibition of RyR by incubation with 1, 10 and 100 µM ryanodine. Effects were observed only with 100 µM and were maximal at 2 h. The amplitude of the Ca 2+ transients decreased in the ventricle of 3 and 5 dpf larvae compared to larvae treated with DMSO for 2 h (Figure  S5A-C ) but the FS did not change significantly (Figure  S5D ). Ryanodine slightly decreased Ca 2+ levels with no difference in the ventricular diameters (Figure  S5A  and E  and Supplementary data 1). It is possible that the relatively small effects found with ryanodine are due to the reported minor contribution of the SR to the Ca 2+ transient in zebrafish, compared to mammals  [19, 20].
Acknowledgements We acknowledge grant  PID2019-111456RB-100  funded by  MCIN/AEI/ 10. 13039/501100011033 ; grants  BFU2015-69874-R  and  EQC-2019-006102-P  funded by  MCIN/AEI/ 10. 13039/501100011033  and by " ERDF A way of making Europe ", by the  "European Union "; grant  SBPLY/19/180501/000223  funded by  Consejería de Educación ,  Cultura y Deportes ,  Junta de Comunidades de Castilla-La Mancha  and " ERDF A way of making Europe ",  European Union ; grants  2019-GRIN-27019 ,  2020-GRIN-29186  and  2021-GRIN-31151  funded by  University of Castilla-La Mancha  and " ERDF A way of making Europe ",  European Union. J. S. -A. held a predoctoral fellowship from  UCLM. M. V. obtained a fellowship from  UCLM  to visit the  Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany.
Neuroimaging using more ecologically valid stimuli such as audiobooks has advanced our understanding of natural language comprehension in the brain. However, prior naturalistic stimuli have typically been restricted to a single language, which limited generalizability beyond small typological domains. Here we present the Le Petit Prince fMRI Corpus (LPPC-fMRI), a multilingual resource for research in the cognitive neuroscience of speech and language during naturalistic listening (OpenNeuro: ds003643). 49 English speakers, 35 Chinese speakers and 28 French speakers listened to the same audiobook The Little Prince in their native language while multi-echo functional magnetic resonance imaging was acquired. We also provide time-aligned speech annotation and word-by-word predictors obtained using natural language processing tools.
Background & Summary In the cognitive neuroscience of language, there is a growing consensus that using more ecologically valid stimuli such as audiobooks might extend our understanding of language processing in the brain  [1] [2] [3]. Compared to traditional factorial designs with a large number of repetitive trials, naturalistic paradigms use stories and dialogues with a rich context and produce results that are generalizable to everyday language use  3, 4. However, prior naturalistic studies have typically been restricted to a single language, which limited neurobiological frameworks for language processing to small typological domains. Here we present Le Petit Prince fMRI Corpus (LPPC-fMRI)  5  , a multilingual fMRI dataset where English, Chinese and French speakers listened to the same audiobook Le Petit Prince (The Little Prince) in their native language (see Fig.
While naturalistic designs opened up a host of new research questions that are not possible to study under tightly controlled experimental designs, the majority of prior naturalistic studies have been restricted to a single language. This limited our understanding of the neural processes of language comprehension to small typological domains. To complement monolingual datasets such as the Narrative Brain Dataset (NBD)  16  , the Alice Dataset  17  , the Narratives dataset  18  and the Mother of Unification Studies  19  , we collected a multilingual fMRI dataset consisted of Antoine de Saint-Exupéry's The Little Prince in English, Chinese and French. A total of 112 subjects (49 English speakers, 35 Chinese speakers and 28 French speakers) listened to the whole audiobook for about 100 minutes in the scanner (see Tables  2  and 4  for the demographics of the participants, data collection procedures, and stimuli information for the English, Chinese, and French datasets).
This stimulus is considerably longer than other datasets (i. e. , 6 minutes on average for the NBD dataset and 12 minutes for the Alice dataset), allowing for testing linguistic phenomena that may not be sufficiently attested in smaller samples. This dataset includes time-aligned speech segmentation, prosodic information and word-by-word predictors obtained using natural language processing tools, ranging from lexical semantics to syntax to discourse information (see Fig. 2  for the annotations available for an example sentence from the English audiobook). The neuroimaging data, as well as the annotations and information about the experimental procedure are shared in a standardized BIDS format on OpenNeuro  5. The LPPC-fMRI facilitates cross-linguistic generalization and helps overcome current statistical and typological limitations in the neurobiology of language.
We stress the importance of considering multiple languages when building and testing neurobiological models of language processing, assuming that the neural substrates and processes of language are shared among speakers of all languages. As shown in previous work examining coreference resolution using the English and Chinese subset of this corpus, the computational model that best explains the neural signature for pronoun processing is generalizable for both English and Chinese  20. These data can be reused to address different research questions with a variety of analytical methods. Future work envisions an expanded LPPC, one that incorporates data from additional neuroimaging modalities, such as electrocorticography (EEG) and magnetoencephalography (MEG). For instance, LPPC-EEG dataset aspires to 26 languages 4.
Methods Participants. A total of 112 subjects listened to the whole audiobook for about 100 minutes in the scanner. Tables  2  and 4  show the summary of the data collection procedure, the stimuli and participants information for the three datasets. English participants were 49 young adults (30 females, mean age = 21. 3, SD = 3. 6) with no history of psychiatric, neurological or other medical illness that might compromise cognitive functions. (A subset of prior work Fig. 1  Schematic overview of the LPPC-fMRI data collection procedures, preprocessing, technical validation and annotation. During data collection (blue), anatomical MRI was first acquired, followed by functional MRI while participants listened to 9 sections of the audiobook. After preprocessing the data (green), behavioral and overall data quality were examined (yellow).
Audio and text annotations were extracted using NLP tools. using the LPP English fMRI dataset used 51 participants' data  [21] [22] [23]. Due to concerns about head movement, only 49 participants' data is released in this corpus. ) They self-identified as native English speakers, and strictly qualified as right-handed on the Edinburgh handedness inventory  24. All participants were paid, and gave written informed consent prior to participation, in accordance with the IRB guidelines of Cornell University. Chinese participants were 35 healthy, right-handed young adults (15 females, mean age = 19. 3, SD = 1. 6). They self-identified as native Chinese speakers, and had no history of psychiatric, neurological, or other medical illness that could compromise cognitive functions. All participants were paid, and gave written informed consent prior to participation, in accordance with the IRB guidelines of Jiangsu Normal University.
Stimuli. The English acquisition. Data acquisition parameters are listed in Table  3  for ease of comparison across English, Chinese, and French. The scanner parameters were the same for English and Chinese with some differences for French. There was a trigger at the beginning of each section and a delay of 8 s (4 TRs) between the trigger and onset of stimulus presentation for all three languages. Preprocessing. MRI data files were converted from DICOM to NIfTI format and preprocessed using AFNI version 16  25. Anatomical. The anatomical/structural MRI scans were deskulled using 3dSkullStrip. The resulting anatomical images were nonlinearly aligned to the Montreal Neurological Institute (MNI) N27 template brain. Resulting anatomical images were used to create grey matter masks. Functional.
The first 4 volumes in each run were excluded from analyses to allow for T1-equilibration effects. The fMRI timeseries were then corrected for slice-timing differences (3dTshift) and despiked (3dDespike). Next, volume registration was done by aligning each timepoint to the mean functional image of the centre timeseries (3dvolreg). Then the volume-registered and anatomically-aligned functional data were nonlinearly aligned to the MNI template brain. Multi-echo independent components analysis (ME-ICA)  26  were used to denoise data for motion, physiology and scanner artifacts. Images were then resampled at 2 mm cubic voxels (3dresample). annotations. Apart from the fMRI timeseries data, we also provide audio and text annotations ranging from time-aligned speech segmentation and prosodic information to word-by-word predictors obtained using natural language processing tools, including lexical semantics, syntax and discourse-level information.
See Fig. 2  for a summary of our annotations. These annotations are available on OpenNeuro too (see the Data records section). Speech segmentation. Word boundaries in the audio were identified and aligned to the transcripts using Forced Alignment and Vowel Extraction (FAVE) (https://www. research. ed. ac. uk/portal/en/publications/ fave-forced-alignment-and-vowel-extraction-suite-version-113(bbc2046d-6768-47c5-b574-2987895b0307). html) and were manually checked by two native speakers each of the three languages. Prosodic information. Root mean square intensity and the fundamental frequency (f0) for every 10 ms of each audio section of the three languages were extracted using the Voicebox toolbox (http://www. ee. ic. ac. uk/hp/staff/ dmb/voicebox/voicebox. html). Word embeddings. Static GloVe embeddings  27  and contextualized BERT embeddings for each word (given its sentential context) in the The Little Prince in the three languages were extracted using the SpaCy package (https://spacy.
Data Records Information and anatomical data that could be used to identify participants has been removed from all records. Resulting files are available from the OpenNeuro repository at https://doi. org/10. 18112/openneuro. ds003643. v2. 0. 0 5. See Fig. 3  for the organization of the data collection. A README file there provides a description of the available content. The scripts used for this manuscript are available on the repository and GitHub (https://github. com/jixing-li/lpp_data). The mutli-echo fMRI data are available as individual timeseries files, stored as: sub-<EN/CN/FR><ID>/func/sub-<EN/CN/FR><ID>_task-lpp<EN/CN/FR>_run-0[1-9]_echo-[1-3]_bold. nii. gz.
Technical Validation Accuracy of participants' responses to the quizzes after each section was calculated to ensure adequate comprehension. To assess fMRI scan quality, we calculated framewise displacement (FD), temporal signal-to-noise ratio (tSNR) and inter-subject correlation (ISC). We also did two whole-brain functional analyses using pitch (f0) and word annotations. These serve to show data quality similar to past work and provide evidence for timing accuracy between fMRI timeseries for participants. Behavioral results. Participants answered four four-choice comprehension questions after each section (36 questions in total). An example question is shown below. Participants performed well with a mean accuracy of 89. 5% (SD = 3. 8) and 86. 4% (SD = 2. 7) for English and Chinese participants, respectively.
Machine learning approaches are becoming an increasingly Cross-linguistic analyses. This multilingual fMRI data is a novel cognitive neuroscience resource since it enables cross-linguistic research. However, there are two points we would like to highlight. Firstly, for each language the dataset was acquired at different sites and we look at interaction effects between sites, not main effects (as seen in Fig. 7 ). Therefore, any specific baseline effects of acquisition would be controlled for (except for potential differences in SNR). Secondly, a group-level analysis, pooling together the data across the three languages would be infeasible. Although English, Chinese, and French follow the same underlying word order (SVO), given the structural, lexical, and prosodic differences between them, it would not be possible to align the same words along a temporal pattern cross-linguistically.
Miscellaneous. The file name patterns reported in the Data Records are meant to be a template. In the actual dataset, some of the runs for a single participant have non-consecutive numbering due to scanning issues or participants needing a break. As a workaround, we created symbolic links for each of the participants' runs by using the Unix in command. As an example, Table  1  illustrates how the runs were renamed for subject 84 in the LPP English dataset to be consistent with the runs [1-9].
pattern specified and execute our scripts across all participants. acknowledgements This material is based upon work supported by the National Science Foundation under grant numbers 1903783 and 1607251, the French Agence Nationale pour la Recherche under grant NCM-NL ANR 16-NEUC-0005-02, and the Jeffrey Sean Lehman Fund for Scholarly Exchange with China at Cornell University. J. L. is supported by the NYU Abu Dhabi Institute under Grant G1001. S. B. is supported by the ONR MURI Award N000141812670. W. -M. L. is supported in part by the Intramural Research Program of the NIA/NIH.
Code availability The code for LPP-fMRI corpus is publicly available at the OpenNeuro repository under code/ subdirectory, and also at the following GitHub repositories: https://github. com/jixing-li/lpp_data, https://github. com/chrplr/ lpp-paradigm. The code includes the presentation scripts for all three languages, the scripts used in technical validation and for preparing this data paper (e. g. , compute_tsnr. py), in addition to code for obtaining annotations (e. g. count_parser_actions. py). Code for certain annotations like word embeddings and POS tagging is not included since there are several publicly available toolkits available to researchers.