dataesr/scientific-paragraphs-categorization huggingface dataset:
Limitations include lack of consecutive enrollment at the site level and that only patients treated with a Boston Scientific DED were candidates. Data regarding referral pathways (ie, whether patients were referred to a specialist by primary care, which symptoms triggered the referral, etc) are not being collected; ergo, refusal rates are not available. Similarly, screening logs being collected centrally; these data are being maintained locally at each site and are not available for analysis at this time. Furthermore, treatment selection bias is a limiting factor, in particular as patients referred for surgical treatment are not included. Finally, the decision was made to include multiracial patientss in the racial/ ethnic group they selected with a smaller sample size. We acknowledge that this could introduce bias should there be a larger proportion of multiracial individuals in ELEGANCE in the future; in such a case, a sensitivity analysis will be performed to determine if this decision introduced a significant bias and, if so, in what direction.
Consider for example the problem of kidney paired donation  (Freedman et al. 2020). A large share of kidney transplants involve a living donor, who is usually a spouse or a relative of the candidate, but all too frequently, the potential donor is a poor match for the candidate they volunteered to help. In such a situation, one solution is to enter all candidates and prospective donors in a database, which is then fed to an algorithm that seeks 2-way, 3-way, or even more complex chains of donations, so that as many candidates as possible find a compatible donor. This algorithm does not simply seek to maximize the number of donations, though, but uses a complex priority scheme that balances many factors such as the age of the candidates and how long they have been registered in the program, their travel distance to the transplantation center, or their baseline chance to find a donor in the general population.
Sample size determination The study was powered to test (i) the difference in prediction for self and others (i. e. , bias blind spot) and (ii) the difference between predictions and actual scores for both self and others. We conducted a MonteCarlo simulation with the R package Superpower  (Lakens & Caldwell, 2021). We used a 2 (target: self vs. other) x 2 (truth estimates: predictions vs. actual scores) within subject ANOVA. The proportion of "true" answers for repeated vs. new statements was the outcome variable. We assumed a small effect (Cohen's d = 0. 20) and a null difference in actual scores for self vs. others. Setting α =. 05, the analysis (number of simulations = 2000) revealed that with 400 participants, we would get (i) a 98% statistical power to observe a self-other difference in predictions, and (ii) 81% power to find a 2x2 interaction between target and truth estimates.
As we estimate approximately a 5% dropout rate (i. e. , participants who will complete only the first session), we planned to recruit a total of 420 participants. We adopted a sequential analysis approach  (Lakens, 2014)  and stopped data collection when 200 participants completed the two sessions and conducted our analysis. As the critical tests (i. e. , main effect of target on predictions and target*truth estimates interaction) were p <. 0294, we stopped our data collection.
Acknowledgements  J. Peña  acknowledges funding from the  French National Research Agency (ANR)  under the  Investments for the Future (Investissements d'Avenir) program  (grant  ANR-17-EURE-0010 ), from the  Institute for Advanced Study (IAS) of the University of Amsterdam , and from the  Max Planck Institute for Evolutionary Anthropology. A. Heifetz acknowledges funding from the  Open University of Israel  research grant  37142. The Julia code used for creating the figures of this paper is publicly available on GitHub (https://github. com/jorgeapenas/ShirkersDilemma).
The EXTEND cohort The EXTEND study is a National Institute for Health and Care Research (NIHR) funded project aiming to collect blood samples along with extensive health information from people with and without health issues  [21]. In the current study, a subset of the EXTEND cohort, consisting of individuals with available genotyping and blood-derived DNA methylation data (n = 1076) was used. The dataset exclusively comprised phenotype data from individuals aged 40 to 75, denoted as the midlife age group.
The EMIF-AD cohort The EMIF-AD cohort is a research initiative focused on collecting and collating comprehensive medical and health-related information from individuals affected by AD and related cognitive disorders  [22]. In this study, besides excluding the individuals outside the midlife age range (i. e. , age < 40 or age > 75), cognitively healthy individuals who were recorded to have converted to MCI or AD during the follow-up period (mean follow-up time ± sd ≈ 2. 3 ± 1. 2 years) were also excluded. This process resulted in a final dataset of 110 individuals with AD, 293 individuals with MCI, and 220 healthy controls with available blood-derived DNA methylation data, genotyping data, and CSF protein markers.
Influence of genetic variation To test the extent to which the variation in the methylation status of the model's CpGs can be explained by genetic variation, the joint and individual variation explained (JIVE) method (r. jive package (v2. 4)  [60] ) was performed on the risk factor model's most important features and the corresponding methylation quantitative trait loci (mQTLs) (clumped cis-and trans-mQTLs, P value < 1e-5, GoDMC database  [19] ). The joint variation, as quantified by the JIVE method, of the risk factor model's CpG β-values and their mQTLs was recorded. In addition, colocalization analysis (coloc package (v5. 2. 2)  [61] ) was performed for each of the model's most important features to assess whether its methylation status and Alzheimer's disease status share a common genetic variant.
Nevertheless, our established MMRS model may act as a starting point for future studies, aiming at further improving the model's predictive performance by testing novel feature selection and machine learning methods, incorporating more omics layers, as well as performing model training on larger (prospective) datasets. In the end, this might bring us closer to the establishment of a reliable blood-based model for the early identification of people at risk of developing dementia, an essential requirement for successful intervention strategies. is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.
I. INTRODUCTION O PTIMIZING video encoding in streaming enhances the Quality of Experience (QoE) for the end-users and minimizes the costs for service providers, predominantly in Video on Demand (VoD) scenarios. However, in live streaming situations where latency is crucial, the video content typically employs a standardized set of encoding parameters without considering optimization. The video segments are separated and encoded at a predetermined set of bitrates and resolutions, Fig. 1 : Rate-Distortion (RD) curves of the Constant Bitrate (CBR) encoding of RushHour s000 and YachtRide s000 video sequences (segments) of VCD dataset  [2]  encoded at 1080p and 2160p resolutions using x265 HEVC encoder at ultrafast preset. Here, VMAF is used as the quality metric. known as the bitrate ladder, to adapt to the network conditions of the end-users  [1].
4 Mbps, meaning that, for bitrates lower than 3. 4 Mbps, 1080p resolution yields a higher Video Multimethod Assessment Fusion (VMAF) 3 score than 2160p. In contrast, for bitrates higher than 3. 4 Mbps, 2160p outperforms 1080p. Conversely, the YachtRide s000 video segment shows that 1080p resolution provides the best Fig. 2 : RD curve of HLS 1 CBR encoding of Characters s000 video sequence (segment) of VCD dataset  [2]  using x265 HEVC encoder at ultrafast preset. Consequently, there is significant storage waste when these representations are stored. performance throughout the entire bitrate range, indicating that 1080p should be the resolution of choice for the entire bitrate ladder. Although per-title encoding schemes  [3] ,  [5] ,  [6]  improve the quality of video delivery, they have been only appropriate for VoD streaming applications because it is computationally expensive to determine the convex-hull.
Research has been conducted on JND, and several surveys have been published  [11] -  [16]. By utilizing JND in video coding, referred to as perceptual  coding, the encoding bitrate can be reduced while still guaranteeing a certain level of video quality or minimizing distortion within a specific bitrate constraint. Furthermore, removing perceptual redundancy information from JND levels compared to traditional video coding methods can lead to additional compression gains  [17]. Most streaming service providers employ Constant Bitrate (CBR) rate-control mode to encode live videos. CBR's consistency in achieved bitrate makes it more dependable for time-sensitive data delivery, especially in live streaming applications. Bitrate overshoot, or the encoded bitrate exceeding internet speeds, is not a concern because CBR-encoded videos are streamed consistently.
A. Test Methodology To validate the performance of the encoding schemes considered in this paper, four hundred sequences (80% of the sequences) from the Video Complexity Dataset  [2]  are used as the training dataset, and the remaining sequences (20%) are used as the test dataset. The dataset needed to train the prediction models is generated as shown in Algorithm 2. E, h, and L features are extracted using VCA v1. 5 17 open-source video complexity analyzer  [32]  run in eight CPU threads using x86 SIMD optimization  [38]. The sequences are encoded at 30fps using x265 v3. 5 2 with the ultrafast preset on a dual-processor server with Intel Xeon Gold 5218R (80 cores, frequency at 2. 10 GHz). The resolutions specified in Apple HLS authoring specifications 1 are considered in the evaluation, i.
6. RF models are trained to predict VMAF (for all resolutions in R) using the CBR encoding dataset (cf. Algorithm 2). • PPTE  [22] , where optimized bitrate-resolution pairs are predicted for JND-aware CBR encoding as shown in Algorithm 1. RF models are trained to predict VMAF and bitrate (for all resolutions in R) using the CBR encoding dataset (cf. Algorithm 2). • HLS cVBR encoding, where the optimized CRF is predicted for the bitrate-resolution pairs of the HLS bitrate ladder, as shown in Fig. 8. RF models are trained to predict CRF (for all resolutions in R) using the cVBR encoding dataset (cf. Algorithm 2). • OPTE cVBR encoding, where the optimized CRF is predicted along with the optimized resolution for the set of bitrates in the HLS bitrate ladder for cVBR encoding. This scheme predicts VMAF for all resolutions in R for a given set of target bitrates.
The resolution which yields the maximum VMAF is chosen as the optimized resolution for the given target bitrate, as shown in Fig. 6. RF models are trained to predict VMAF and CRF (for all resolutions in R) using the cVBR encoding dataset (cf. Algorithm 2). For PPTE and JTPS encoding, the parameters, b min , and b max , are set as 0. 145 Mbps and 16. 8 Mbps, respectively, to compare with the HLS bitrate ladder. The average target JND function (v J ) is considered as two  [16] , four, and six  19  based on current industry practices. Accordingly, v max is set as 98, 96, and 94, respectively, to comply with the target JND value. First, the pre-processing time (τ p ), i. e. , latency in streaming due to the time taken for video complexity feature extraction,  and the inference time of the models to predict the optimized bitrate-resolution-CRF triples are determined to evaluate the first-pass encoding time.
Second, the VMAF, log(b), and CRF prediction models are assessed in terms of the prediction accuracy using the coefficient of determination (R 2 ) score and Mean Absolute Error (MAE) compared to the ground truth values. The achieved VMAF, bitrate, and CRF recorded in the cVBR encoding dataset are considered ground truth values. Third, the relative importance of the features used is evaluated using the SHapley Additive exPlanations (SHAP) values  [39]. The encoding schemes' rate-distortion (RD) curves are analyzed for selected video sequences (segments) of various video content complexities. Bjøntegaard delta rates  [40]  BDR P and BDR V refer to the average increase in bitrate of the representations compared with that of the reference bitrate ladder encoding scheme to maintain the same PSNR and VMAF, respectively.
B. Experimental Results This section presents the results of JTPS. For evaluating the pre-processing time (τ p ), E, h, and L features are extracted at an average speed of 370 frames per second over the entire dataset. The average inference speed of the random forest models for the bitrate ladder and CRF prediction is 250 frames per second. Since the video complexity feature extraction and the encoding bitrate-ladder prediction are executed as concurrent processes, the first-pass encoding speed is the minimum of the speed of the video complexity feature extraction and the encoding bitrate-ladder prediction. Hence, the average first pass speed of JTPS is 250 frames per second, which makes it suitable for live-streaming applications. As shown in Table  I , the state-of-the-art methods have pre-encoding steps to determine convex-hull, making them unsuitable for live streaming applications.
The moderating roles of perceived usefulness and ease of use While guanxi determines the impact of relational connections on business relationships through social exchanges, AI algorithms are introduced to help improve the effectiveness and efficiency of business transactions. Prior technology acceptance model (TAM) research suggests that perceived ease of use and perceived usefulness are likely to attenuate differences in users' reactions to new technologies  (Wu & Lederer, 2009). The TAM is employed to gain a better understanding of how, if at all, AI impacts business relationships between bankers and their SME clients. For example, AI technology uses data provided by customers and big data collected from the Internet to construct clients' credit assessments from information such as litigation from third parties to reduce information asymmetries  (Cihon, Kleinaltenkamp, Schuett, & Baum, 2021)  and provide equal opportunity for all (John-Mathews, Cardon, & Balagué, 2022;  Wang, Sheng, & Zhou, 2022).
Samples Our research aims to explore and examine the impact of guanxi on the dyadic business relationships between bankers and their SME clients when AI-assisted smart loan services are introduced. There are two versions of the questionnaire: one is tailored to bankers (i. e. , Questionnaire B), and the other is designed for SME clients (i. e. , Questionnaire C). Each questionnaire is developed for its target participants. According to the data of the China Banking & Insurance Regulatory Committee (2022) in 2022, approximately 46,000 financial institutions were operating in the country. From this group, six are state-owned commercial banks, 12 are publicly listed, 129 are city-owned, 41 are foreign, and the rest are private or county-organised credit co-ops (http://www. cbirc. gov. cn/branch//view/pages/common/ItemDetail.
html?docId=1070089&itemId=863). Fifty financial institutions comprising state-owned and publicly listed banks, foreign banks, and credit co-ops were short-listed based on their market shares (92%), geographic spread (across the country), and cultural (social and institutional) characteristics. We randomly selected branches from the list with the help of a China-based global marketing research agency specialising in B2B data in China. We mailed 1000 general invitation letters to branch, loan, and risk managers. A total of 346 responses were initially sent back. Questionnaire B was sent to follow up on these responses. 283 out of 346 surveys were returneda response rate of 82%. For the SME clients, we adopted a snowballing approach. Each of the 283 participating bankers was asked to provide the names and contact information of two or more of their frequent SME clients, particularly owners, major shareholders, finance managers, and key staff involved in commercial loan applications for companies.
The modified items in Questionnaires B and C can be found in Tables  2  and 4  in the appendices. All English-language questionnaires were translated into Chinese and back-translated following a standard back-translation procedure  (Brislin, 1986). In particular, an expert panel of six bilingual professionals was recruited to identify and resolve any inadequacies in the forward and backward translations to ensure the intended meanings of the original English versions were conveyed clearly. A pre-test was conducted with nine bankers and 12 of their SME clients after completing the translation. The pre-test participants were asked to confirm the clarity, applicability, and usefulness of the translated questionnaires, and a few minor modifications were made based on their feedback. Furthermore, face validity was examined, and participants were excluded from the later data collection accordingly.
At the beginning of the questionnaires, the bankers and SME representatives were asked a screening question to ensure they possessed adequate knowledge and experience of AI-assisted smart loan services. Finally, anonymity and confidentiality assurances were given at the onset of the questionnaire to encourage the authentic disclosure of the perceptions of all participants  (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). The survey was designed to collect participant data from both ends of the dyadic relationship to explore variations in the perceptions of the business relationship. Thus, Questionnaires B and C were developed using the same measurements, with minor modifications depending on the participant category. Validity and reliability tests were performed with AMOS V. 23 before testing the significance of the conceptual models  (Ab Hamid, Sami, & Sidek, 2017).
The assessment of convergent validity is effectuated by testing composite reliability and the average variance extracted (AVE), which demonstrate whether the level of correlation of multiple indicators of the same construct is consistent  (Fornell & Larcker, 1981). In addition, discriminant validity is considered by making use of the  Fornell and Larcker (1987)  criterion, which compares the square root of each variable's AVE and the correlations of the latent variables  (Hair Jr, Hult, Ringle, & Sarstedt, 2021) , shown in Tables  3  and 5 , (see appendices), suggesting that the discriminant validity is satisfactory. Moreover, the sampling errors of the SME clients' and bankers' data sets were tested to ensure sample representation. The results suggest that, at a 95% confidence interval level, the sampling error for the SME client data set and banker data set are 5% and 6% respectively.
Results We followed a stepwise procedure in the data analysis. Validations from two-group analysis of a multiple-indicators multiple causes (MIMIC) of structural equation modelling (SEM) and variance analysis of regression (one-way ANOVA) were conducted. Confirmatory factor analyses were also employed to examine and ensure the validities (i. e. , convergent and discriminant validity) and reliability of the variables in the proposed model. Tables  2 3 4 5 (see appendices) show the rigor of the adopted and modified measurement sets in the present research processes and the trustworthiness of its findings. First, a MIMIC 3  of SEM was conducted using AMOS V. 23 to ensure the influence of the latent variables on the higher-order constructs that are the three dimensions of guanxi (ganqing, renqing, and xinren) and the three dimensions of bank engagement via AI in the two data sets (bankers and their SME clients) (cognitive process, affection, and activation).
24 to further confirm the result of the SEM analysis. For example, Leven's statistic rejects the null hypothesis of equal population variances when the p-value is less than 0. 05, suggesting metric variance was not verified. Population variance unequally influences the managerial role in the relationship performance between bankers and their SME clients. After determining the variance between the two data sets, further analysis could proceed. The results are illustrated in Table  6  (see appendices). Furthermore, a two-sample variance comparison of Mann-Whitney-Wilcoxon (MWW, McKnight & Najab, 2010) and a twosample z-test  (Takiar, 2023)  were performed on the bank engagement via AI for bankers and SME clients employing AMOS V. 23. The result of a two-sample z-test reveals that the z-value is greater than z critical twotail at a 95% confidence interval, suggesting the bank engagements via AI for bankers and their SME clients are significantly different.
The direct effects H1 predicts that guanxi leads to a higher level of relationship performance. In the SMEs' dataset, guanxi is significantly and positively related to relationship performance (β = 0. 691, p =. 000). Similar results are found in the bankers' dataset as the relationship between guanxi and relationship performance is significant and positive (β = 0. 368, p =. 000). Therefore, H1 is supported. The strength of the relationships between guanxi, bank engagement via AI, and relationship performance is different between bankers and their SME clients. The bankers' dataset shows a significant and positive effect of guanxi on bank engagement via AI (H2a: β = 0. 445, p =. 000); likewise, the SME clients also reveal a significant and positive but weaker effect of guanxi on bank engagement (H2b: β = 0.
378 p =. 000). Furthermore, the effect of bank engagement via AI on relationship performance in the banker dataset is positive and significant (H2c: β = 0. 386, p =. 000), the relationship between bank engagement via AI and relationship performance is positive and significant in the SME client dataset (H2d: β = 0. 225, p =. 000); therefore, H2a, H2b, H2c, and H2d are supported.
The mediations: Indirect effects H2e predicts that bank engagement via AI mediates the relationship between guanxi and relationship performance. As shown in Table  6  (see appendices), the SME client dataset, revealing an indirect effect of guanxi on relationship performance through bank engagement via AIassisted smart loan services, shows a significant and positive indirect relationship (β = 0. 096, S. E. = 0. 022; bootstrapped 95% BC CI = [0. 054, 0. 141]). The banker dataset also reveals a significant and positive indirect relationship between guanxi and relationship performance through bank engagement (β = 0. 172, S. E. = 0. 048; bootstrapped 95% BC CI = [0. 091, 0. 281]). The results suggest that bank engagement in both datasets positively and significantly affects relationship performance.
The results suggest a complete mediation of bank engagement via AI in the banker and SME client datasets. Thus, H2e is supported. Figs. 1 and 2 illustrate the differences between the two datasets. (See Figs. 3  and 4. ) 4. 3. The moderating role of perceived ease of use SEM was performed on both datasets to test H3, which concerns the moderating effects of perceived ease of use. The results show that the SME client data demonstrate that perceived ease of use negatively moderates the relationship between guanxi and relationship performance (coeff/se = -0. 085/0. 043, p =. 048; CL LL = [-0. 170, -0. 001]). This leads to an interesting inquiry on whether ease of use weakens the relationship between guanxi and relationship performance. When clients find AI-assisted smart loan services easy to use, the impacts of guanxi are reduced.