dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Purpose of the package SoundChunk is user-oriented and help: • detecting sounds of interest in audio file • building modular databases once sounds of interests have been identified This package does not provide a 100% accurate detection but allows users to check the detections, edit and clean a pre-processed labeled audio to then build modular databases. The user thus keeps control on its data extraction for further analyses. This package is built using in R (R Core Team 2023) and uses seewave  (Sueur, Aubin, and Simonis 2008)  (v2. 2. 0) and tuneR  (Ligges et al. 2023) (v1. 4. 4)  packages. Detection of sound of interest is based on the timer() and ffilter() functions, thus working on the smooth envelop of a sound file within a frequency band of interest. The package allows bridging R and Audacity ("Audacity®" 2017), a free open-source and easy-to-use software for viewing, labeling and editing audio files.
Actions chunking -the process of detecting and labeling chunks. dispatching -the process to extract chunks from an audio file based on their boundaries, which are stored in a label track (audacity-like csv files) and store them individually. chopping -the process of cutting a long recording into standardized pieces (whatever the recording contains). Main content of V1. 0. 0 1. Create databases from entire directories containing audio and label track files: • dispatch_chunks() : extract, name and save chunks locally • a grouped behaviour (one directory created per wanted groups of chunks), • a flat behaviour (all chunks from all recordings in a single directory) • to generate them, the package needs: • a parameters file with the match between the origin (a. k. a. the file names of the audio files without extension) and all common information associated to each recording.
This file must be a tab separated (. tsv) file, with headers corresponding to the parameters associated to each recording. The origin column must be present. • a naming_rule file: containing information about the chunk and about the recording the user may want to see in the file name of each chunk and in the final log of the database. set_naming_rule() will help creating it and set_naming_rule_path() will allow using an already created naming_rule file. 2. Chunk a mono recording: • detect_chunks_interactive(): plot spectrograms and detections plots on audio files. Assist the user in finding robust parameters for chunking, according to the type of chunks. This program interactively asks the user: • if they want to change the detection settings and keep track of them, • to semi-manually label chunks to create semi-manual databases later on, • if they want some fragments of background noise to be labeled for further use.
Installation and usage The first time used, the package needs to be downloaded it from a GitLab server. Once it has been downloaded and locally stored, only loading the library will be needed to use the package. Install SoundChunk, using the remotes library Use SoundChunk remotes::install_gitlab("AvelyneV/soundchunk", host = "framagit. org", dependencies = TRUE, build_vignettes = TRUE) library(soundchunk) Information about SoundChunk, especially vignettes, or any of its function ?name_of_the_function.
NAMING_RULE and NAMING_RULE_PATH In order to generate databases, a match between the context of a recording (e. g. date, field, treatment), the properties of a chunk (e. g. low bleat, tap) and the name of the chunk file is needed. Such a correspondence may be created interactively using set_naming_rule(). Once created, this information is stored into a global variable NAMING_RULE and stored in the file system, so that it may be called later using set_naming_rule_path(). By default, NAMING_RULE and NAMING_RULE_PATH are NULL. N. B. when creating a naming_rule, the name of the factor we input must match with the column names of the parameters file (type of information about the recording) and to the column names the label tracks (type of information about the chunk).
• finding the information contained in the labels of the label track • setting up a naming rule to choose what should be the name of the chunks and in the log of the chunk meta data • dispatching the chunks into different types of databases • creating log files to monitor the chunks and their information of audio files in a directory • associated to each recording, a label track file whose name holds a different extension but the same name before extension. A label track contains three tab-separated columns: start, end, label of each chunk (could have been exported from Audacity for instance) • a parameters file containing information about the recordings What the directory looks like: Architecture of input data What the label track from Audacity looks like: Input label track from Audacity What a parameters file looks like.
Objective: We aimed to evaluate the use of a machine learning model to predict the need for intubation within 24 hours by using a combination of CXR and EMR data in an end-to-end automated pipeline. We included historical data from 2481 hospitalizations at The Mount Sinai Hospital in New York City. Methods: CXRs were first resized, rescaled, and normalized. Then lungs were segmented from the CXRs by using a U-Net algorithm. After splitting them into a training and a test set, the training set images were augmented. The augmented images were used to train an image classifier to predict the probability of intubation with a prediction window of 24 hours by retraining a pretrained DenseNet model by using transfer learning, 10-fold cross-validation, and grid search. Then, in the final fusion model, we trained a random forest algorithm via 10-fold cross-validation by combining the probability score from the image classifier with 41 longitudinal variables in the EMR.
Data Sources The Mount Sinai Hospital currently uses 3 main electronic health record platforms: Epic (Epic Systems), Cerner (Cerner Corporation), and Laboratory Information Systems Suite (Single Copy Cluster Soft Computer). Data are aggregated from all 3 systems into a harmonized data warehouse. We received admission-discharge-transfer events from Cerner, laboratory results from Laboratory Information Systems Suite, and clinical data (ie, vital signs and nursing assessments) from Epic. Electrocardiogram results were obtained from the MUSE cardiology information system (GE HealthCare Technologies, Inc). To assemble the CXR data set, we obtained raw DICOM (Digital Imaging and Communications in Medicine) files from the picture archiving and communication system platform (GE HealthCare Technologies, Inc).
Transfer Learning Approach The segmented CXRs were then fed into a pretrained DenseNet-201  [29]  model. Multimedia Appendix 1 shows the architecture of the DenseNet-201 model. The DenseNet model was pretrained on RGB images (3 input channels) from ImageNet data set and on 1000 classes. Model input and output dimensions were changed to fit a grayscale image binary classification task. We also modified the architecture by adding a linear convolutional layer with a rectified linear unit activation function and dropout and used the LogSoftmax function to obtain the final probability output. The model prespecifications were as follows: Adam optimizer, the loss function was binary cross-entropy, and the epoch size was 50. The framework used was PyTorch (version 1. 01). Both segmentation and classification model training were performed using PyTorch libraries in Python  [30]  and trained with graphic processing unit clusters on Amazon Web Services in a secured network.
Sampling Strategy for EMR Features Given the crisis nature of the pandemic, clinicians caring for this cohort collected data such as vital signs, laboratory results, electrocardiograms, and nursing assessments, based on clinical judgment and resource availability rather than standard protocols during the early phase of the crisis. Thus, to create longitudinal (time-series) data for each observational variable, we included the 3 most recent assessments available before the prediction time (Figure  5 ). Missing values for each variable were imputed using the median value across the cohort  [35]. When less than 3 assessments are available for a particular variable, the available values are placed in the most recent time slots, and the oldest time-slot value is imputed with the cross-cohort median for that variable.
EMR Feature Selection From a total of 56 routinely collected EMR variables from the hospital, an optimal set of 41 variables was selected for the development of the predictive models (Multimedia Appendix 3). The variables initially removed included those with 90% or higher missing values and highly correlated variables  [36]  (above 0. 7). We then performed recursive feature elimination  [37]. In this approach, a single feature is removed at each step, and the model is evaluated on the test set. The quality of the fit to the data is measured using AUROC. Variables whose removal does not significantly alter the AUROC are eliminated from the feature set.
Model Testing and Statistical Methods For each of the developed models, performance was evaluated on the test set and on the holdout set (which was not used for model development), and the model-derived class probabilities were used to predict intubation within 24 hours with a default threshold of 0. 5. Predictions less than the threshold were categorized as negative. Sensitivity, specificity, accuracy, positive predictive value, negative predictive value, F 1 -score, AUROC, and area under the precision recall curve (AUPRC), along with bootstrap 95% CIs, were estimated for evaluating the screening tool's performance. Group comparisons were performed using a 2-sided Student t test or Kruskal-Wallis for continuous variables as appropriate and chi-square test for categorical variables. All analyses were performed using SciPy in Python.
We study the short-run, dynamic employment effects of natural disasters. We exploit monthly data for 70 3digits NAICS industries and 78 Puerto Rican counties over the period 1995-2019. Our exogenous measure of exposure to natural disasters is computed using the maximum wind speed recorded in each county during each hurricane. Using panel local projections, we find that after the ''average'' hurricane, employment falls by 0. 5% on average. Across industries, we find substantial heterogeneity in the employment responses. Employment increases in some industries while in others employment decreases after a hurricane. This heterogeneity can be partly explained by input-output linkages.
Wind speed at the county level We use satellite data from the National Oceanic and Atmospheric Administration (NOAA) Tropical Prediction Center. We look at the storms' best tracks in the Caribbean Sea over the period 1995-2017 to construct the maximum wind speed associated with each hurricane  hitting Puerto Rican county , i. e. A best track contains the full history of a hurricane, with information at 6-hour intervals on latitude, longitude, date, and wind speed at its eye. First, we linearly interpolate the storms' best tracks at every kilometer. For each interpolated kilometer, we compute the set of coordinates for the position of the eye (landmark ℎ) and the wind speed at the eye,  ℎ. For each county that falls in the vortex associated with a landmark ℎ, we use the HURRECON model (see  Boose et al.
, 1994 Boose et al. , , 2001 Boose et al. , , 2004) )  to compute the sustained wind velocity at the county's centroid,  ℎ. 3 n order to improve the precision of our measure and obtain a good proxy of the impact of hurricanes on economic activity, we do not use the simple geometrical centroid of each county. Instead, using population data from the 2010 Census at the block level and the geometrical centroid of each census block, we construct a populationweighted centroid for each of the 78 counties of Puerto Rico. This correction allows us to compute the maximum wind speed affecting populated areas and to give less weight to strong winds hitting forests or other areas with no economic activity. Finally, we obtain one measure of windspeed per county and hurricane by retaining the maximum windspeed to which a county was exposed: 𝑤 𝑐𝐻 = max ℎ∈𝐻 {𝑤 𝑐ℎ }.
Puerto rico employment data We use employment data from the Quarterly Census of Employment and Wages (QCEW) by the Bureau of Labor Statistics of the United States (BLS). The QCEW contains monthly data on employment and quarterly data on wages at the county and county-industry level. To the best of our knowledge, this level of detail is impossible to find in developing countries. We focus our attention on employment in the private sector. To give a broad idea of the economic structure of the Puerto Rican economy, during our sample period, the private sector employed on average 674,800 people. Of those workers, 18. 4% were employed in Retail Trade (NAICS 44-45), 17. 2% in Manufacturing (NAICS 30), 9. 7% in Health care and social assistance (NAICS 62), 9. 1% in Accommodation and Food Services (NAICS 72), 8.
7% in the Administrative and Waste services (NAICS 46), and 7. 1% in Construction (NAICS 23). In terms of employment dynamics, manufacturing has witnessed a secular decline. Most services display an increase over time, while the construction sector seems characterized by an inverted U-shaped dynamics. Within the manufacturing sector, the most important industries in terms of average employment in our sample period are NAICS 325 (''Chemical manufacturing''), NAICS 311 (''Food manufacturing''), and NAICS 315 (''Apparel manufacturing''). While (unsurprisingly) apparel manufacturing employment displays a negative trend, chemical manufacturing employment shows an inverted-U shape. We also use the Local Area Unemployment Statistics from the BLS to get county-level data at monthly frequency on the number of unemployed and the total labor force.
8 ur second specification is therefore the following: 𝛥𝑋 𝑖𝑐,𝑡+𝑘 = 𝛼 𝑘 + 𝛾 𝑘 2 𝑆 𝑐𝑡 + ∑ 𝑝 𝛽 𝑝 𝛥𝑋 𝑖𝑐,𝑡-𝑝 + 𝜈 𝑖𝑐 + 𝜈 𝑖𝑡 + 𝜖 2 𝑖𝑐,𝑡+𝑘 , (3)   represents employment, (for each county  and industry  at monthly frequency) or the average weekly wage (at quarterly frequency). All of the other variables are defined as in Eq. (  2 ). Now, we can introduce county-industry specific fixed effects (  ), together with industry-time fixed effects (  ). As before, our sample period runs from 1995M1 to 2019M11. We have data for a panel for 78 counties (GEO) and 70 3-digits industries (NAICS). The advantage of using these more detailed data is the possibility to control for a richer set of fixed effects. The main disadvantage is that using county-by-industry level variables implies dealing with a lot of reported zeros.
Two industries are classified as ''neutral'': NAICS 481 (''Air transportation'') and NAICS 483 (''Water transportation''). Two other industries are instead classified as ''weakened'': NAICS 485 (''Transit and ground passenger transportation'') NAICS 487 (''Scenic and sightseeing transportation''). This particular example shows the importance of using detailed industry data to better gauge the employment effects of natural disasters. In Appendix E, we propose a Table where we report the classification (S, N, or W) also for different time horizons  (3, 6, 12, 18, months)  for each industry, thus giving a simple way to better grasp the dynamic employment effects of disaster shocks in each of the industries. Discussion and Mechanisms. The positive effects in the construction sector are consistent with other studies and indicate an intense process A.
We concentrate on the strengthened industries that we could match with the Input-Output data (excluding the construction sectors). 16 Fig. 5  illustrates a plot of the coefficients from Table  2  against the share of construction sectors as buyers of the industry output. The relation is starkly positive and statistically significant. The univariate regression has an  2 = 0. 61. Using data from the NBER-CES Manufacturing Industry Database, we verified that this correlation is robust also after controlling for the industry's labor share and the skill composition of its labor force. 17 We conclude that input-output linkages are likely to play a key role in the transmission of disaster shocks to employment across different industries. Finally, we notice that the lack of impact of hurricanes on the employment in many important manufacturing sectors (which might 15 For example, Table  2  clearly indicates that not all industries in the manufacturing sector respond in a similar way.
Conclusion In this paper, we study the short-run, dynamic employment effects of natural disasters. Using monthly data for 70 3-digits NAICS industries and 78 Puerto Rican counties over the period 1995-2019, we find an average decline in employment following a disaster shock, which masks an extensive heterogeneity at the industry level. A part of this heterogeneity can be explained by input-output linkages. We are persuaded that the key qualitative insights we provide are valid also for other contexts, such as developing countries, where data limitations make impossible the type of analysis performed in this paper. Moreover, the fact that some industries contract and some others expand following natural disasters suggests a new potential concept of resilience: adaptability-driven employment resilience, defined as the potential opportunity for workers to reallocate from the contracting industries to the expanding ones in the aftermath of a natural disaster.
Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:  Alessandro Barattieri  reports financial support was provided by  Social Sciences and Humanities Research Council of Canada. Martino Pelli  reports financial support was provided by  Social Sciences and Humanities Research Council of Canad  and by the  Swiss National Science Foundation. Jeanne Tschopp  reports financial support was provided by  Social Sciences and Humanities Research Council of Canada  and by the  Swiss National Science Foundation. Acknowledgments This work was supported by the  Social Sciences and Humanities Research Council (SSHRC)  [grant number  039367 ] and by the  Swiss National Science Foundation (SNSF).
e. , a case of polysemy)? Have they influenced each other? Which fields have been most influential to the evolution of this concept? Here, we address these questions by studying a corpus of scientific literature indexed by the Semantic Scholar open database  [15]. First, we observe an exponentially growing interest in the topic, with an author in 154 contributing to a paper mentioning (de)centralization in its title or abstract in 2021. Then, we map the literature on (de)centralization by focusing on the subset of relevant articles and clustering them according to their semantic and citation information. This way, we discover that different academic fields have separately contributed to this topic. We hence study how the different clusters have influenced each other, showing how much more transfer of knowledge between different academic areas is happening in recent years.
Step 1: data collection The first step consists in collecting the academic publications related to the concept of (de)centralization, or potentially to other concepts. To perform a large scale analysis of the academic literature, we exploit the possibility to access the publicly available Semantic Scholar Academic Graph (S2AG, pronounced "stag"), which provides monthly snapshots of research papers published in all fields  [15]. Launched in 2015 by the Allen Institute for Artificial Intelligence (AI2), Semantic Scholar provides this corpus as an open access database with the specific scope of facilitating scientific analysis of academic publications. It contains about 203. 6 million papers (1st Jan. 2022 snapshot), 76. 4 million authors, and 2 billion citations. Moreover, this database recently incorporated the Microsoft Academic Graph (MAG)  [17] , which was shut down at the end of 2021  [18].
From this corpus we extracted the data about papers that contain the root string "centrali" in words of the title or abstract, to capture possibly all variations of words related to the concepts of Centralization and Decentralization (nouns, adjectives, etc. ). In this way, we also incidentally captured articles written in different languages, mainly Portuguese, French and Spanish, and also a minority of unrelated articles (e. g. , biology articles involving plant species including "centrali" in their name). More information on the frequency of the words containing such root string can be found in Table  S1  in the Additional file 1 (SM). The resulting dataset has 425,144 papers characterized by a series of attributes. Among these, of particular interest to us there is the title, the abstract, the authors, all inand out-citations (respectively citations and references), the year of publication, and the fields of study, which were determined based on machine learning field classifiers leveraging on the existing MAG taxonomy and classification  [19].
Step 2: hierarchical clustering In the Semantic Scholar corpus almost each paper is associated to a list of fields of study. However, these are high-level, as there are in fact only 19 fields of very heterogeneous sizes (see Table  S3  in the SM for details on how many papers are classified in each field of study). Moreover, sometimes the fields are not correctly assigned. In the second step of the pipeline, we hence use a multilayer hierarchical stochastic block model (hSBM)  [20, 21] , developed to find statistically significant clusters at multiple hierarchical levels for the analysis of text data with multiple data types. Here, in fact, we consider two layers. The document layer -where links represent citations between papers-and the text layer -a bipartite network between documents and the words present in their titles.
The method naturally produces clusters of documents and topics (word clusters), incorporating the information from both layers in the process. Furthermore, as the name suggests, the model produces a hierarchical clustering, providing a richer structure of both article clusters and topics, which captures both small clusters and topics and how they are related to each other in a higher level structure. We consider only the papers in our dataset that have a non-empty title and contain at least one citation or reference to another paper in the dataset (42. 7% of the initial dataset), as we are interested in how the concept of (de)centralization evolved in the academic literature, and citations are the most natural proxy for how knowledge is transferred. We use title texts, instead of abstracts, for various reasons: firstly, because the title is more frequently available than the abstract (see Table  S2  in the SM); secondly, because the title has the advantage of being more distilled compared to abstracts  [22] ; lastly, because titles contain a significantly smaller number of words than abstracts, allowing us to obtain a text layer similar to the document layer in terms of number of edges by simply cutting out words present in less than 5 documents.
It is indeed well known that the hSBM performs optimally when both layers have roughly the same size, otherwise the smaller layer is effectively ignored by the algorithm  [21]. The filtered dataset hence consists of 181,605 documents and 15,381 different words, summing up to 590,215 document-to-document citation links and 1,396,830 document-to-word links. To ensure the robustness of our results, we performed 100 iterations of the algorithm. Notice that the number of clusters and levels of granularity obtained is not fixed, but is automatically suggested by the algorithm. By running the algorithm multiple times, we aimed to capture the inherent variability and uncertainty in the Monte Carlo partitioning process. Subsequently, the consensus partition is calculated by maximizing the overlap with all the partitions from the 100 runs.