dataesr/scientific-paragraphs-categorization huggingface dataset:
OCTA image acquisition. OCTA imaging was performed with the Optovue RTVue XR Avanti spectraldomain instrument (Optovue Inc, Fremont, CA, USA Version 2016. 2. 0. 35) with phase 7 AngioVue software. Images were taken using 840 nm light source, 45 nm bandwidth, and an A-scan rate of 70,000 scans per second. Each scan consisted of 608 B-frames, composed of a set of 304 A-lines acquired 2 times at each of the 304 raster positions. The scanning area used in this study was 3 × 3 mm, centered on the fovea. The software included the 3D Projection Artifact Removal (PAR) algorithm  9. Images with Signal Strength Index (SSI) below 70 were excluded. The Quality Index (QI) (range 1 to 10) accounted for the signal strength and 2 additional factors: motion artifacts and image sharpness. Only images with QI of 7 or greater (manufacturer's recommendation) and without large movement or shadow artifacts were considered for further analysis.
Vessel density measurements and FAZ parameters. Using the AngioAnalytics software, we calculated VD as the percentage of the image occupied by retinal blood vessels within the SCP, ICP, and DCP layers in the fovea, parafovea and a 3-mm-diameter circle centered on the fovea. The foveal area was defined as a 1-mmdiameter circle centered on the fovea. The parafoveal area was defined as a ring from the edge of the foveal circle to the edge of a 3-mm-diameter circle centered on the fovea (Fig. 2 ). The FAZ size (in mm 2 ) was automatically calculated by the AngioAnalytics software using the nonflow area measurement tool. FAZ perimeter (mm) was defined as the length of the contour demarcating the FAZ. FAZ acircularity index (AI) was calculated as the ratio of the FAZ perimeter to the perimeter of a circle with an equal area, AI = FAZ Perimeter/√(4 × π × Area)  13.
We also recorded FD-300, a parameter calculated by the AngioAnalytics software as retinal capillary density from ILM to OPL in a 300 μm wide region around the FAZ (Fig. 3 ). Analysis of the choriocapillaris blood flow area. Blood flow area (BFA), the percentage of a retinal image occupied by pixels corresponding to blood vessels, was calculated automatically after the image capture process by AngioAnalytics software  15. In this study we obtained choriocapillaris BFA within 1 mm-and 3 mmdiameter circular zones centered manually on the fovea. The choriocapillaris BFA in the parafoveal region was determined by subtracting the BFA of the 1 mm-diameter circular area from the BFA of the 3 mm-diameter circular area centered on the fovea (Fig. 2 ).
Upon review of literature, we have noted that in most reports of OCTA parameters in retinal vascular diseases the race of subjects is not specified, which may introduce a confounding bias and affect data interpretation. Our findings of baseline differences in macular capillary parameters among young healthy black and white subjects suggest that these OCTA metrics may vary in populations in ways that are not related to disease. It is important to consider race when analyzing retinal vasculature among different study groups and creating normative databases involving OCTA parameters. Furthermore, as our understanding of the chorioretinal vascular anatomy and physiology evolves with the help of novel imaging modalities, we may choose to select biomarkers that are less susceptible to variation by race in the healthy population.
VD has been suggested as a potential biomarker of retinal vascular diseases; however there may be a difference in baseline VD within the three distinct capillary plexuses across races as shown in our study. Thus, race needs to be considered as a possible confounder when analyzing macular capillary density Table  3. Macular capillary parameters measured using optical coherence tomography angiography in white and black subjects. Data provided show the results of a mixed effects regression correlating race and macular capillary parameters, adjusting for 2 eyes of the same patient and axial length. Data are provided as mean ± standard deviation. Fovea refers to the central 1-mm diameter circular area. Parafovea refers to the 3-mm diameter area excluding the central 1-mm diameter. P* value calculated controlling for 2 eyes of the same patient, and axial length.
Since our study included only young healthy subjects, these results may not be generalizable to older populations and patients with comorbidities. Our analysis of the chorioretinal capillary parameters is limited by the 3 × 3 mm scan acquisition. Further studies analyzing larger scanned areas can contribute more insights into chorioretinal capillary anatomy and physiology. Additionally, our study utilizes a manual segmentation algorithm, which is limited in its execution by the resolution of the acquired OCTA images and the AngioAnalytics software. The strengths of the study include recruitment of a well-matched patient cohort, and utilization of the latest OCTA software with projection artifact removal allowing analysis of the distinct capillary plexuses.
Introduction The reliability of automatic evaluation metrics is an important factor for progress in artificial intelligence tasks, enabling the comparison and improvement of the proposed systems. The design of reliable metrics for natural language generation (NLG) systems is very challenging, and still an open research problem:  Novikova et al. (2017)  and  Peyrard (2019)  showed that current metrics do not correlate well with human judgments, and argued for the development of new evaluation metrics. Among NLG tasks, summarization is one of the most difficult to evaluate automatically. For a given document, the number of possible correct outputs is much larger than for other tasks such as machine translation. Thus, when only a single reference summary is given -as is typically the case for large-scale summarization datasets, the correlation of standard automatic evaluation metrics with human judgments is low  (Louis and Nenkova, 2013).
Our contributions are as follows: • We show that, by unifying the precision and recall-based QA metrics, we obtain a more robust metric; • We propose a method to learn the saliency of the generated queries, allowing to integrate the notion of information selection; • We evaluate QU E S TEV A L on two corpora containing annotated summaries from CNN/Daily Mail and XSUM datasets. The proposed metric obtains state-of-the-art results in terms of correlation with humans judgments, over all the evaluated dimensions. Notably, QU E S TEV A L is effective at measuring factual consistency, a crucial yet challenging aspect for summarization.
Related Work Summarization Metrics The most popular evaluation metric for summarization is ROUGE  (Lin, 2004) , which computes the recall of reference ngrams in the evaluated summary. Other n-grams based metrics have been proposed such as CIDEr  (Vedantam et al. , 2015)  and METEOR  (Lavie and Agarwal, 2007) , but none of them correlates better with humans according to SummEval, a recent large study conducted by  Fabbri et al. (2020). Recent works have leveraged the success of pretrained language models. Zhang et al. (2019b)  proposed BERTScore, which uses BERT  (Devlin et al. , 2018)  to compute a similarity score between the reference and the evaluated text. However, its performance is similar to that of  ROUGE (Fabbri et al. , 2020). Several works have explored using natural language inference (NLI) models to evaluate the factual consistency of summaries  (Kryściński et al.
(2020). Query Weighting In  Scialom et al. (2019) , all questions are considered equally important, i. e. the weight W (q, D) = 1 for every query q ∈ Q G (D). However, since a summary necessarily has a constrained length, an effective summary should contain the most important information from the source. To account for this, we introduce a question weighter, which is trained to distinguish important questions from anecdotal ones. We leverage existing summarization datasets to create training data for the weighter: given a source document D, each question q ∈ Q G (D) is labeled as important if the corresponding human summary contains the answer, as computed by the QA component applied on the summary (i. e. Q A (S, q) = ). W (q, D) denotes the probability that q is important for D. Note that the question weighter only concerns recall, and therefore is not applied when computing precision.
Summarization Datasets To evaluate QU E S TEV A L, we measure its correlation with human judgments on different datasets:  2020 ), it is one of the largest human-annotated datasets for summarization. Derived from CNN/Daily Mail  (Nallapati et al. , 2016) , it consists of 12,800 summary level annotations. To ensure diversity, the summaries were generated from 16 different summarization models, including extractive and abstractive architectures. To assess quality, three experts annotated four dimensions: i) Consistency: the proportion of facts in the summary corresponding to facts in the original text; ii) Coherence: how well-structured and well-organized is the summary; iii) Fluency: how fluent the summary is to read; and, iv) Relevance: the ratio between important and excess information in the summary.
Question Answering & Generation To train our Q G and Q A models, we used the SQuAD-v2  (Rajpurkar et al. , 2018)  factoid question answering dataset: it is composed of (paragraph, question, answer) triplets, and includes unanswerable questions. Note that QG can be seen as the dual task for QA: any QA dataset can be used for QG by switching the generation target from the answer to the question. Lastly, we found it helpful to train our QA model using additional synthetic unanswerable questions. This is done by considering a shuffled version of the dataset, where each question is randomly assigned to a paragraph from another triplet of the dataset. We consider these additional samples, with flipped contexts, as unanswerable. All experiments in this paper, except otherwise specified, use this additional data to improve identification of unanswerable queries.
Baselines Metrics As baselines, we considered the following: N-gram based ROUGE  (Lin, 2004)  is the most widely used evaluation in summarization. This metric measures the recall of reference n-grams in the evaluated summary. Conversely, BLEU  (Papineni et al. , 2002)  computes the precision of summary n-grams in the references. METEOR  (Lavie and Agarwal, 2007 ) is a variant that uses stemming, synonyms and paraphrastic matches. LM based Leveraging recent progress in language modeling (LM),  Zhang et al. (2019b)  proposed BERTScore: for each token of the summary, the maximum cosine similarity is computed over contextualized token embeddings of the reference summary, and the overall mean is reported. QA based SummaQA  (Scialom et al. , 2019 ) is a recall oriented metric, with questions generated from the source document.
Most of the large scale datasets provide only one reference per example in their test set (e. g. CNN/Daily Mail and XSUM), a fact that highlights the importance of searching for more reference-efficient alternatives. With regards to sample efficiency, QA-based metrics do not require any references. We expect Relevance to be better measured by Recall oriented metrics, and less so for Consistency. This is con- firmed in the results, where SummaQA correlates better with Relevance than Consistency (26. 2 vs 8. 3), and vice versa for QAGS (9. 1 vs 20. 4). By unifying and extending the two, QU E S TEV A L allows to take both dimensions into account, improving the average correlation by 18% (28. 4 to 33. 5). The dimension benefiting the most from our question weighter is Relevance (+4%, from 37. 5 to 39.
However, QU E S TEV A L does not require any reference. Therefore, the improvement over the other metrics grows larger as the number of references used decreases. Furthermore, QU E S TEV A L enables the evaluation of systems even when no gold reference is available. Query Weighter There is no unique answer to the question "What makes a good summary?": it depends on the reader's point of view, which makes summarization evaluation challenging. For instance, given a contract, the seller and the buyer could be interested in different information within the same document. To instantiate the weighter W , we learn a specific dataset policy: "what kind of questions are likely answered in the CNN/Daily Mail training summaries?" This is a reasonable heuristic given that editors created the summaries following their specific policy.
g. Text Simplification  (Scialom et al. , 2021) , Image Captioning  (Lee et al. , 2021) , and Data To Text  (Rebuffel et al. , 2021). In future work, we plan to extend QU E S TEV A L to multilingual scenarios such as Machine Translation and Multilingual Summarization. seq2seq models. We use the T5-base model  (Raffel et al. , 2019)  implemented in Hugging Face  (Wolf et al. , 2019). We trained our models on a single Nvidia RTX 2080 Ti GPU with 11G RAM. For selecting entities as the answers candidates, we use spaCy 2 6. For all the experiments, we used the default hyper-parameters. For all the variations of hyper-parameters and models, we tested QuestEval on QAGS-XSUM data, in order to keep SummEval unseen at test time. When we compute QuestEval precision or recall only , we observe a significant improvement over both QAGS and SummaQA.
EEG recording EEG data were recorded using a 64-channel ActiveTwo system (BioSemi ® , The Netherlands). Two electrodes were applied on the left and right outer canthi of the eyes and one below the left eye to record the electrooculographic activity. An additional electrode was placed on the tip of the nose for offline referencing. During recording, impedances were kept <10 kΩ. EEG signal was recorded with a sampling rate of 512 Hz and filtered at 0-104 Hz.
Pre-processing A 0. 3-Hz digital high-pass filter was applied to the EEG signal. Ocular artifacts were removed by applying Independent Component Analysis (ICA) as implemented in EEGLab. Blink artifacts were captured into components that were selectively removed via inverse ICA transformation. Thirty-two components were examined for artifacts and one or two components were removed in each subject. Muscular and other recording artifacts were discarded manually. EEG data were recorded continuously and time locked to each trial onset. Trials were extracted over a 700-ms analysis period, from 100 ms pre-stimulus to 600 ms post-stimulus. ERPs were baseline corrected and digitally filtered with a low-pass frequency cut-off of 30 Hz. The first three trials of a sequence, as well as trials occurring after deviant or target stimuli were not included in averaging.
Source analysis We performed source reconstruction to estimate the anatomical location of electric sources that could explain ERP data. We used the Curry 8 software (Compumedics USA, Inc. ) which implemented the methodology proposed by Wagner et al. Distributed source analysis was performed with the sLORETA method (standardized low-resolution brain electromagnetic tomography) for current density reconstruction (CDR) and a realistically shaped three-compartment boundary element method as head model  41. CDR results for each subject, each emotion (Anger, Neutral), and each condition (Deviant, Equiprobable) were analyzed using the CDR SnPM algorithm (CDR-Statistical non-Parametric Mapping), which is able to extract brain locations through time with significantly different activations between Groups, Emotions, and Conditions, including both main effects and interactions  42.
Marcel et al. (2019)  (hereafter M19) performed the first application of the JED-SAD model to real data by qualitatively reproducing the spectral evolution of GX 339-4 during the 2010 outburst observed by RXTE. A similar study has been recently extended to three others outbursts of GX 339-4  (Marcel et al. 2020, hereafter M20). These authors did not directly fit the data given the large number of observations as well as the lack of a consistent reflection model component. Instead, they produced a large grid of spectra for a set of parameters (r J , S. Barnier et al. : Clues on jet behavior from simultaneous radio-X-ray fits of GX 339-4 ṁin ) and fitted each simulated spectrum with a disk + power law model. This provided spectral characteristics (disk flux, power law luminosity fraction, X-ray spectral index) that were then compared to the best fit results obtained by fitting the RXTE/PCA data with a similar disk + power law model  (Clavel et al.
e. , r J and ṁin ). To do so, we develop the required tools to apply our JED-SAD model to standard fitting software (like xspec,  Arnaud 1996). We focus in this paper on the simultaneous radio-X-ray coverage of the XrB GX 339-4 during the lifetime of the RXTE 1 However a better result was obtained when using a constant, but different, fR for the rising and decaying phase with f rise R > f decay R (G. Mar- cel, private communication). satellite  (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012). This corresponds to four major outbursts starting in  2002, 2004, 2007 and 2010. The data selection is discussed more precisely in Sect. 2. Our fitting procedure and first fit results, using Eq. (  1 ) for the radio emission, are discussed in Sect.
We selected X-ray spectra from the RXTE-PCA archive of GX 339-4 during the 2000-2010 decade 2. The data processing is detailed in  Clavel et al. (2016). Since the instrumental background was generally found to be of the order of, or larger than, the source emission above 25 keV, we limited our spectral analysis to the 3-25 keV energy range of the PCA instrument. We plotted the 2000-2010 PCA X-ray light curve of GX 339-4 in Fig. 1. During this period, GX 339-4 undergoes four complete outbursts, in  2002, 2004, 2007 and 2010, hereafter   Rise (a)  Decay (b)  X-ray (c)   2008 were not selected for this study 3  since they may be intrinsically different from the ones accomplishing an entire HID. We follow  Clavel et al. (2016)  for the definition of the hard state periods of each outburst. We report in Table  1  the corresponding starting and ending Modified Julian Dates (MJD) of both the rising and the decaying hard state phases.
We then use an automatic fitting procedure using the pyxspec library (a python interface to xspec). We fit the X-ray spectra with the following xspec model : tbabs * (atable(JEDtable) + atable(SADtable) + kdblur * atable(Refltable)) Where JEDtable, SADtable and Refltable are the xspec tables for the JED, SAD and reflection spectra respectively, and kdblur a convolution model of xspec to take into account the relativistic effects from the accretion disk around a rotating black hole (according to the original calculations by  Laor 1991). The parameters r J and ṁin are tied together between each table and we set the inner radius of kdblur to the inner radius of the SAD (i. e. , r J ). In kdblur, we freeze the index of the disk emissivity to 3 (its default value), the outer disk radius to 400 R g and the inclination to 30 •.
MapReduce framework Big Data is a large and heterogeneous collection of datasets which makes it difficult to process using traditional data processing tools. Nowadays, the collected datasets come mostly from social networks and scientific applications. To overcome the computational and storage of Big data challenges, various solutions have been successfully proposed. Among the popular approaches, there is the famous Hadoop MapReduce framework  [15]. In this section, we will focus on how distributed computing program works over the Hadoop MapReduce Model. First, the Hadoop framework and the MapReduce programming model will be briefly presented. Then, we describe how this system processes units of data key, value in parallel MapReduce approach.
Hadoop framework The Apache Hadoop is one of the most popular open source framework for the clustered environment that allows reliable, scalable, and distributed storage. It also helps with the processing of large datasets through the simple programming models. It manages computer clusters built from single to thousands of machines, each offering local computation and storage. The failure of a node in the cluster is automatically managed by re-assigning its task to another node  [15]. The project Apache Hadoop includes four fundamental modules: Hadoop Common or Hadoop Core , HDFS, YARN and Hadoop MapReduce. The following figure schematizes these components. Figure  2  Overview of Apache Hadoop. [1]   • Hadoop Common or Hadoop Core provides essential services and basic processes such as abstraction of the underlying operating system and its file system.
It also contains the necessary Java libraries and scripts required to start Hadoop. The Hadoop Common package also provides source code and documentation, as well as a contribution section that includes different projects from the Hadoop Community [15]. • Hadoop Distributed File System (HDFS) is a distributed file system developed by Apache Hadoop. It ensures high-throughput storage and access to application data on the community machines thus providing very high aggregate bandwidth across the cluster [15], high fault tolerance and native support of large data sets  [27]. • Hadoop Yet Another Resource Negotiator (YARN) is a platform to manage cluster resources and schedule tasks. It was added in the Hadoop 2. 0 version to increase the capabilities by solving the limit of 4000 nodes and Hadoop's inability to perform fine-grained resource sharing between multiple computation frameworks  [28].
Data generation method We randomly generated a large variety of WFST data sets in two phases. First, we generated a set of deterministic finite automata using FAdo library  [31] , which is an open source project providing a set of tools for the symbolic manipulation of automata. FAdo is based on enumeration and generation of initially connected deterministic finite automata  [33]. In the second phase, we implemented a function that randomly adds weights and some nondeterministic degree on the output symbols over transitions of the finite automata of the first phase with a uniform distribution. Our generation technique produces WFSTs based on two parameters: the number of states m and the input alphabet size k. Thus, one can define the transition density of the generated WFST as the ratio |E| k×m , the final state density as the ratio |F | m and consider a unique initial state as in  [34].
Communication cost analysis Let us evaluate the communication cost of the proposed methods on large scale WFST data sets. The communication cost is defined as the total number of keyvalue pairs transferred from the map phase to the reduce phase. It can be optimized by minimizing the replication rate parameter i. e. the number of input copies sent to the reducers. The following table gives us the relationship between the considered data set sizes and the communication cost: The obtained results show clearly that in general, the communication cost using the hybrid mapping method is minimal in all situations i. e. for all combinations of state sizes and alphabet sizes. This is due to the fact that the number of the transition copies sent to the reducers with this method is less than the other ones as proved formally in Proposition 4.
In this application, the public key is a composition of n+1 finite automata, and, the private key is the n + 1 weak inverse finite automata of them  [4]. The second target application is in natural language processing to handle tasks such as translation between two languages, using one or multiple intermediate languages and for speech recognition. We also hope to extend and, test this method on a multi-nodes cluster environment with the GPU and OpenMP to accelerate the composition algorithm for large-scale computing. The principal steps of MapReduce  [30].