dataesr/scientific-paragraphs-categorization huggingface dataset:
Cluster analysis Across the 8,793 text statements considered in this analysis, with one text segment collected as part of one listener self-initiated EMA, a total number of 145,926 word forms and 6,245 different word forms were recorded. About 44% of all individual word forms (2,769 out of 6,245), corresponding to 2% of the total number of words (2,769 out of 145,926) were recorded only once. Cluster analysis revealed that 97. 9% (8,606 out of 8,793) of the available text data could be classified into the resulting clusters, indicating that the results are representative of the corpus. The clustering revealed two branches (see Figure  3 ), one consisting of four clusters and the other consisting of three clusters. The branches and clusters were named by the researchers based on their expert domain knowledge, while interpreting the characteristic words (see Figure  4 ) and characteristic texts (see Table  1 ) derived for each cluster, based on a ranking of χ 2 -values.
professional in their systematic review, which can either positively or negatively impact hearing aid use and experiences. For example, the patient"s attitude towards hearing aids, expectation management, practical handling skills training, and patientcentered information counselling. We did not identify personrelated themes in the cluster analysis presented here. While it is unclear how clinicians introduced hearing aid wearers to the EMA-functionality used for the data collection, it is possible that listeners were asked or were more inclined to describe in-themoment device-rather than person-related experiences. Also, the clinical data was collected through a feature of a mobile application that was activated in the fitting software by a clinician, thereby likely not capturing experiences prior to hearing aid fitting.
Even so, individual text statements from the current dataset provide anecdotal evidence for the person-related themes identified by Oosthuizen et al. (38). Some reports, for example, describe experiences regarding information counselling and practical handling skills, e. g. , "I"m wondering how long the aids last with rechargeable batteries. Can they be replaced if they no longer charge? (…)", "I unpaired my hearing aids and now I don't know how to pair them again help!!!", "it would be nice to listen to music off my phone at times like a pair of ear buds. if these do it please let me know how to do it. ", Other reports describe experiences regarding expectations, e. g. , "(…) Saw a singer in the pub at night. It was hard to make out what people were saying. Went back to lip reading. Maybe I was expecting more.
Limitations To our knowledge, this is the first study to report on a large data set of self-initiated EMAs, containing open-text statements provided by hearing aid wearers. It is important to acknowledge that the data explored was not collected for research purposes. The data was logged to support hearing aid wearers and clinicians during the hearing rehabilitation process, i. e. , for purposes of counselling and fine-tuning by collecting in-the-moment feedback. Using clinical data for research purposes offers real-world insights that can support continuous improvements in health care (55) and personalized care (56), but comes with limitations. Verheij et al. (57)  and Dillard et al. (58) , for instance, describe the use of electronic health records for research purposes and how each step in the process, from a clinically relevant event (e.
Materials Acoustic data were recorded with a hypercardioid headset microphone (AKG C520), an external sound card (Edirol UA25) and Audacity (v. 2. 1. 0) on a portable computer. The sampling rate was set to 44100 Hz. We had access to the sound attenuated studio of the local television channel for our recording sessions. Our speakers sat in a chair and read a word list embodied in a carrier sentence at a normal speech rate. The word list (see Table  3 ) was constructed with the segments /i a u ¨z/ occupying the nuclei of monosyllabic words starting with /p pH m n ts tsH s/. With different tones, they form single noun phrases, presented to the speakers in Chinese characters. Each word was produced within the carrier phrase [ki 44.
Data analysis The recorded acoustic data were segmented and annotated using Praat  (Boersma & Weenink 2018). It was challenging to determine a clear boundary between a sibilant onset and the following apical vowel (for example in  [tsz] ). We labelled our data by taking the first voicing pulse detected in Praat as the beginning of the apical vowel. This is illustrated in Figure  2. Measurement criteria were established after visual inspection of both spectrograms and waveforms. The following parameters were considered: (i) FRICATION IN APICAL VOWELS. This aspect was obtained through visual inspection of the acoustic waveforms and spectrograms for all utterances. Fricated apical vowels were those exhibiting a degree of turbulent airflow on the spectrogram in the high and mid frequencies.
Data acquisition and analysis The ultrasound data were recorded with the Ultrasound Stabilisation Headset (Articulate Instruments Ltd. 2008) and the Articulate Assistant Advanced software (AAA, V217. 03) (Articulate Instruments Ltd. 2012). The probe used in data acquisition was a portable microconvex ultrasonic probe with a diameter of 40 mm. The speakers sat in a comfortable chair; the headset was then adjusted to the anatomical specificities of each speaker, so that it did not move during the recording, nor did it cause discomfort. Data were recorded first in the mid-sagittal plane, and then in the coronal plane, with a small pause between two sessions during which the headset was removed. The headset kept the ultrasound probe in the mid-sagittal plane and the probe was pointed to the anterior of the tongue in order to have the best image of the tongue tip.
We considered the area within the junction of all traces of all segments to be reliable (e. g. for FS3, between the fan lines 9 and 29 in Figure  8 , the angle between the two fan lines was approximatively 46 • ). This junction area could be easily identified as the tongue shapes of [i u ¨a z] all passed through. We used traces within this area as this was the only reliable way to generalise comparisons between segments. These areas were defined individually based on data from each speaker. 12 The palatal traces were obtained by averaging six separate water-swallow trials, which were performed once at the beginning and once at the end of each session on both mid-sagittal and coronal planes.
Acknowledgements The authors are grateful to the participants of this study. We would like to thank the Associate Editor  Dr Alexei Kochetov  and two anonymous reviewers for their valuable comments and careful proofreading, which greatly improved the quality of the manuscript. All errors remain our own. This work has partially benefited from a government grant managed by the  Agence Nationale de la Recherche  under the  Investissements d'Avenir programme  with the reference  ANR-10-LABX-0083  (contributing to the  IdEx University of Paris  - ANR-18-IDEX-0001 , LABEX-EFL).
3 Hediating effect 4 Moderation effect 5 A B S T R A C T According to its function as increasing disposable income, pension is believed to reduce absolute poverty of recipients. However, nowadays, researchers haven't reached the consensus on whether pensions reduce the relative poverty or not. Given the data from the China Health and Retirement Longitudinal Study (CHARLS) 2018, this study investigates on if pension has positive effect on the reduction of relative poverty by performing the heteroskedastic probit model with 2SLS and IV estimators. The result shows that: (1) Pension has a noticeable ani-poverty effects. Three relative poverty line charts indicate a decrease by 4. 8 %, 4. 28 % and 4. 39 % in the probability of beneficiaries falling into relative poverty for each 1 % increase in pensions.
Data source and processing The data in this paper are obtained from the China Health and Retirement Longitudinal Study (CHARLS) data, which covers 150 county-level units, 450 village-level units, and 17,000 people in about 10,000 households. The CHARLS national baseline survey was conducted in 2011 and returned in 2013, 2015, and 2018, and the questionnaire includes basic personal information, personal health status, pension, income, consumption and other information, which are well suited for this study in terms of survey population, coverage, questionnaire content and data quality, and are a high-quality microdatabase for conducting pension and elderly poverty studies. In this paper, the 2018 survey data were selected as the baseline regression analysis, and the 2015 data were used for robustness testing.
Conclusion and policy recommendations Using data from the China Health and Retirement Longitudinal Study (CHARLS) 2018, this paper explores the impact of pensions on relative poverty from both theoretical and empirical perspectives using a combination of heteroskedasticity probit, Iv-2sls-probit, and propensity score matching methods. The study shows that pensions significantly alleviate the relative poverty of residents. Under the three relative poverty lines, the probability of individuals falling into relative poverty decreases by 3. 83 %, 4. 28 % and 4. 39 % for each 1 % increase in pensions; the heterogeneity test finds that the pensions alleviate the greatest relative poverty effect in the central region, followed by the west and the lowest in the east. The mediating effect test shows that pensions strengthen their ability to alleviate poverty through intergenerational Fund from children.
OBJECTIVE The current study used a longitudinal network model to (1)  investigate the mechanism by which social support protects individuals from developing a high severity of depression under a stressful situation. We performed the CLPN analysis on data obtained from the Covid Mental Health Survey (COMET) cohort. According to the network theory, the (de)activation of a highly central symptom will spread through the network and (de)activate a greater number of other symptoms. 10  By estimating and comparing the centrality indices of depressive symptoms in the networks, we were able to identify which specific symptoms were playing key roles in the protective effect of social support on depression. Loneliness was a frequent consequence of the COVID-19 pandemic due to public health interventions that promoted social isolation and has a moderately significant effect on depression.
Study design and data source The data for this study were from a large international cohort study-the COMET Survey, started in May 2020-during the early world-wide outbreak of COVID-19. More details of the survey are available online (https://osf. io/bgtsf/). In brief, the survey was conducted simultaneously via an online tool (Survalyzer) in 13 countries affected by the pandemic. Sampling was conducted by sharing links to the survey via social media, instant messaging platforms and emails. Only adults (over 18 years old) who consented to participate were eligible. Participation was voluntary and participants were free to skip questions or withdraw from the study at any time. Participants included in the baseline were invited for future study waves via email invitations. Participants who completed the full survey entered a survey lottery as compensation for their participation.
The study protocol was approved from relevant ethical review boards from lead centres/institutions involved in the COMET study (see online supplemental material). Baseline data were collected from 8013 participants. Our study focused on the time window between wave 1 (May-July 2020) and wave 2 (September-October 2020), the early stage of pandemic when most people perceive COVID as stressful. For the longitudinal analysis, a subsample of 4500 individuals who completed the follow-up survey was initially included. For the on June.
Open access We included different domains of social support in this study. Both structural (OSSS1) and instrumental support (OSSS3) are associated with significant reductions in depressive symptoms, but there was a smaller effect from emotional support (OSSS2) to depression or loneliness. The results were consistent with one previous study reporting associations between depression remission and social network size, practical support and loneliness severity, but not with emotional support. 34  Our findings suggest providing instrumental support such as financial support or practical help could be more beneficial than strategies focused on emotional support after stressful events. There are several limitations of the current study. First, the data were obtained from an online survey using convenience sampling and this likely introduced sampling bias.
This study shows the importance of access to social networking platforms for online learning. Previous experience and intention to use appear to play a key role in the choice of emerging technologies for academic purposes. To arrive at these results, the authors mobilize the revised UTAUT model. One of the conclusions is that users' behavioral intention to adopt E-learning is significantly affected by effort expectancy and performance expectancy. Overall, I find this work well conducted, and the database available to the authors is rich in 702 responses, but as it stands I have my doubts about the theoretical and applied contributions of this publication. On reading this article, the following 7 comments lead me to ask several questions:.
2022 ), and Africa  (Adler et al. 2014 ;  Blinkhorn et al. 2021 ). The identi cation of discoidal and Levallois products therefore appears widespread in lithic analysis across various traditions of lithic studies and is designed to create a comparable dataset, explore speci c technological adaptations in different ecological contexts, and discuss long-term techno-cultural traditions and technological change. One special category of such products are backed akes that exhibit remnants of the core on one of their lateral edges. Backed akes are usually classi ed into two technological categories: "core edge akes" (eclat débordant) and "pseudo-Levallois points". A third category, "core edge akes with a limited back" (éclat débordant à dos limité), has also been de ned (Liliane Meignen 1993; Lilliane Meignen 1996;  Pasty et al.
sampling, surface reconstruction and transformation into PLY les. Finally, all les were decimated to a quality of 50,000 faces using the Rvcg R package  (Schlager 2017 ). The protocol for the digitalizing landmarks on akes is based on previous studies  (Archer et al. 2018 (Archer et al. , 2021)). This included the positioning of a total of three xed landmarks, 85 curve semi-landmarks, and 420 surface semi-landmarks  (Bookstein 1997a (Bookstein , 1997b;; Gunz et al. 2005 ; Gunz and Mitteroecker 2013; Mitteroecker and Gunz 2009). This resulted in a total of 508 landmarks and semi-landmarks. The three xed landmarks correspond to both laterals of the platform and the percussion point. The 85 curve semilandmarks correspond to the internal and exterior curve outlines of the platform (15 semi-landmarks each) and the edge of the ake (55 semi-landmarks).
The principal components (PCs) of a PCA aim to capture as high a variance as possible for the complete data (James et al. 2013a), and PCs that capture the highest variance need not necessarily be the best for classi cation. For the present work, PCs that represent 95% of variance are selected as predictors for training the machine learning models. The threshold of 95% of variance is arbitrarily selected because it balances retaining most of the dataset variance with a reduced number of variables. The identi cation of best PCs for classi cation is performed automatically by the machine learning models using the caret v. 6. 0. 92 package (M. Kuhn 2008). PC from geometric morphometrics are hard to interpret as a result of reducing dimensionality through the identi cation of linear combinations of variables.
The present study tests SVM with linear, radial, and polynomial kernels. Naïve Bayes: computes class probabilities using Bayes's rule  (Weihs et al. 2005 ). As mentioned above, 66. 91% of akes fall into the de nition of core edge akes with a limited back, resulting in an unbalanced dataset. To counter the unbalanced nature of the experimental dataset, upsampling was undertaken for the two minority classes, and down-sampling was undertaken for the majority class. Up-sampling categories in a dataset can be considered inappropriate for training machine learning models because it increases the over t (samples used in the test set to evaluate the model are likely to have already been used in the training set). However, here, the up-sampling of the two minority groups increases the discreteness of these groups but does not affect the potential overlap with the majority class (core edge akes with limited backs).
On the other hand, down-sampling results in missing information because some of the data are removed. Random up-and down-sampling is conducted to obtain a balanced dataset and train the models. Because model performance metrics depend on random up-and down-sampling, this process is repeated 30 times, extracting model performance metrics each time and averaging the values. The model with the best performance metrics is then trained again with thirty cycles of up-and down-sampling. The reported variable importance and confusion matrix from which model metric performance are extracted are obtained from these additional cycles of down-and up-sampling. 3D meshes and data from landmark positioning and Procrustes alignment are already publicly available through the repository of a previous publication using the same experimental collection (Bustos-Pérez et al.
PCA and machine learning model performance The PCA results (Fig. 4 ) show that the 25 rst principal components account for 95% of the variance in the dataset, with PC1 accounting for 21. 39% of variance and PC25 accounting for 0. 36% of variance. This represents a substantial reduction in dimensionality from the original number of variables  (1, 524)  and is lower than the sample size (139). Figure  5  presents the accuracy values for each model after their respective 30 cycles of random up-and down-sampling. Supported vector machines with a polynomial kernel had the highest average value for general accuracy (0. 837), closely followed by the random forest model (0. 829). The K-nearest neighbor model had the lowest average value for accuracy (0. 681), followed by LDA model (0. 690). Supported vector machines with a polynomial kernel had the lowest standard deviation in accuracy values (0.
018). This indicates that, in addition to being the most precise model on average, SVM with a polynomial kernel is also the most consistent model, being less affected by random up-and down-sampling. The minimum accuracy value of SVM with a polynomial kernel is 0. 801, indicating a high accuracy, even when up-and down-sampling result in similar objects. 2  presents performance metrics of the SVM with a polynomial kernel for the classi cation of the three products. The prevalence/no information ratio was kept constant at 0. 33 for all three categories as a result of random up-and down-sampling to obtain balanced datasets. In general, the three products had high values for performance metrics, with Pseudo-Levallois points standing out in this regard. Following pseudo-Levallois points, core edge akes had the second-best general values, with a sensitivity close to 0.
The present study included all backed akes from a series of experimental recurrent centripetal cores. As a result, backed artifacts that fall within the de nition of "core edge akes with a limited back" were the overwhelming majority (n = 93, 66. 91%). Consequently, it was necessary to use up-and down-sampling techniques to obtain balanced datasets (Ganganwar 2012; Kumar and Sheshadri 2012). The most upsampled product (pseudo-Levallois) is also the one with the highest values for identi cation metrics, probably as a result of over tting (the model is classifying repeated samples from the training set). Given the strict de nition adopted to classify a backed artifact as either a core edge ake or a pseudo-Levallois point, their morphological variability is limited, and the likelihood of their overlapping is small.
While global patterns of human genetic diversity are increasingly well characterized, the diversity of human languages remains less systematically described. Here, we outline the Grambank database. With over 400,000 data points and 2400 languages, Grambank is the largest comparative grammatical database available. The comprehensiveness of Grambank allows us to quantify the relative effects of genealogical inheritance and geographic proximity on the structural diversity of the world's languages, evaluate constraints on linguistic diversity, and identify the world's most unusual languages. An analysis of the consequences of language loss reveals that the reduction in diversity will be strikingly uneven across the major linguistic regions of the world. Without sustained efforts to document and revitalize endangered languages, our linguistic window into human history, cognition, and culture will be seriously fragmented.
However, an accurate understanding of the actual structural diversity of languages, the factors that shape that variation, and what is at stake when the world loses languages has been hampered by the lack of accessible, systematically sampled, global data. For example, the World Atlas of Language Structures (WALS)  (8)  has incomplete genealogical coverage  (9)  and 84% missing data (see fig. S1 ). Here, we introduce Grambank-a systematic sample of the structural diversity of the world's languages. Grambank is designed to be used to investigate the global distribution of features, language universals, functional dependencies, language prehistory, and interactions between language, cognition, culture, and environment. The Grambank database currently covers 2467 language varieties, capturing a wide range of grammatical phenomena in 195 features, from word order to verbal tense, nominal plurals, and many other well-studied comparative linguistic variables.
The dataset includes both varieties classified as "languages" and "dialects" [70 dialects representing 46 languages, resulting in a total of 2430 unique languages;  (1) ]. The coverage spans 215 different language families and 101 isolates from all inhabited continents and geographic regions (see figs. S2  and S3 ). Languages are important to cultural identity, health, the preservation of traditional knowledge and institutions, and as a unique window into human history, culture, and cognition  (10) (11) (12). However, languages are vanishing at a rate that rivals our biodiversity crisis  (13, 14). It is estimated that without intervention approximately one language will be lost every month in the next 40 years  (15). This tragic situation and its detrimental consequences have prompted the United Nations (UN) to recently announce the UN Decade of Indigenous Languages  (16).
The Grambank dataset is uniquely positioned to showcase the diversity of the world's languages and the knowledge that we are currently in danger of losing. Here, we use the Grambank data to answer four long-standing questions about global linguistic diversity that have previously been difficult to answer in a rigorous quantitative manner. What are the relative roles of genealogical inheritance and geographical diffusion in shaping grammatical diversity? How constrained is grammatical evolution? What are the world's most unusual languages, and what will the consequences of language loss be on our understanding of linguistic diversity?.