AN EVALUATION OF HYPERALIGNMENT ON REPRODUCIBILITY AND PREDICTION ACCURACY FOR FMRI DATA

Functional magnetic resonance imaging (fMRI) is a neuroimaging technique which measures a person's brain activity using changes in the blood flow in response to neural activity. Recently, resting state fMRI (rs-fMRI) has become a ubiquitous tool for measuring connectivity and examining the functional architecture of the human brain. Here, we used a publicly available rs-fMRI dataset to investigate the performance of the hyperalignment algorithm, on several fMRI analyses. The research employs the use of the image intra-class correlation coefficient and functional connectome fingerprinting to evaluate the reproducibility of both the unaligned and hyperaligned data, and developed a predictive model to investigate whether hyperalignment improves the prediction of certain behavioral measures. Overall, our results illustrate the utility of the hyperalignment algorithm for studying inter-individual variation in brain activity

The stability of physicians’ risk attitudes across time and domains

unding No specific funding was received for this work. Xuemin Zhu's PhD studentship was funded by the Elphinstone Scholarship Scheme, University of Aberdeen. This research used data from the MABEL longitudinal survey operated by the University of Melbourne and Monash University. Funding for MABEL was provided by the National Health and Medical Research Council (2007–2016: 454799 and 1019605); the Australian Department of Health and Ageing (2008); Health Workforce Australia (2013); The University of Melbourne, Medibank Better Health Foundation, the NSW Department of Health, and the Victorian Department of Health and Human Services (2017); and the Australian Government Department of Health, the Australian Digital Health Agency, and the Victorian Department of Health and Human Services (2018). The Health Economics Research Unit is funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorates. The funders were not involved in the study design, collection, analysis, and interpretation of data, the writing of this article or the decision to submit it for publication. The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the funders that provide institutional support for the authors. Acknowledgement The authors acknowledge the physicians who spent their valuable time participating in the MABEL longitudinal survey and the research team that designed and administered the survey. The authors would like to acknowledge valuable comments and suggestions at the PhD Student Conference in Behavioural Science 2018, European Health Economics Association Student-Supervisor Conference 2019, and the Health Economists' Study Group Summer Meeting 2019.Peer reviewedPostprin

TQ-Net: Mixed Contrastive Representation Learning For Heterogeneous Test Questions

Recently, more and more people study online for the convenience of access to massive learning materials (e.g. test questions/notes), thus accurately understanding learning materials became a crucial issue, which is essential for many educational applications. Previous studies focus on using language models to represent the question data. However, test questions (TQ) are usually heterogeneous and multi-modal, e.g., some of them may only contain text, while others half contain images with information beyond their literal description. In this context, both supervised and unsupervised methods are difficult to learn a fused representation of questions. Meanwhile, this problem cannot be solved by conventional methods such as image caption, as the images may contain information complementary rather than duplicate to the text. In this paper, we first improve previous text-only representation with a two-stage unsupervised instance level contrastive based pre-training method (MCL: Mixture Unsupervised Contrastive Learning). Then, TQ-Net was proposed to fuse the content of images to the representation of heterogeneous data. Finally, supervised contrastive learning was conducted on relevance prediction-related downstream tasks, which helped the model to learn the representation of questions effectively. We conducted extensive experiments on question-based tasks on large-scale, real-world datasets, which demonstrated the effectiveness of TQ-Net and improve the precision of downstream applications (e.g. similar questions +2.02% and knowledge point prediction +7.20%). Our code will be available, and we will open-source a subset of our data to promote the development of relative studies.Comment: This paper has been accepted for the AAAI2023 AI4Edu Worksho

The Application of Downhole Vibration Factor in Drilling Tool Reliability Big Data Analytics - A Review

In the challenging downhole environment, drilling tools are normally subject to high temperature, severe vibration, and other harsh operation conditions. The drilling activities generate massive field data, namely field reliability big data (FRBD), which includes downhole operation, environment, failure, degradation, and dynamic data. Field reliability big data has large size, high variety, and extreme complexity. FRBD presents abundant opportunities and great challenges for drilling tool reliability analytics. Consequently, as one of the key factors to affect drilling tool reliability, the downhole vibration factor plays an essential role in the reliability analytics based on FRBD. This paper reviews the important parameters of downhole drilling operations, examines the mode, physical and reliability impact of downhole vibration, and presents the features of reliability big data analytics. Specifically, this paper explores the application of vibration factor in reliability big data analytics covering tool lifetime/failure prediction, prognostics/diagnostics, condition monitoring (CM), and maintenance planning and optimization. Furthermore, the authors highlight the future research about how to better apply the downhole vibration factor in reliability big data analytics to further improve tool reliability and optimize maintenance planning

Aqueous electrosynthesis of an electrochromic material based water-soluble EDOT-MeNH2 hydrochloride

2\u27-Aminomethyl-3,4-ethylenedioxythiophene (EDOT-MeNH2) showed unsatisfactory results when its polymerization occurred in organic solvent in our previous report. Therefore, a water-soluble EDOT derivative was designed by using hydrochloric modified EDOT-MeNH2 (EDOT-MeNH2·HCl) and electropolymerized in aqueous solution to form the corresponding polymer with excellent electrochromic properties. Moreover, the polymer was systematically explored, including electrochemical, optical properties and structure characterization. Cyclic voltammetry showed low oxidation potential of EDOT-MeNH2·HCl (0.85 V) in aqueous solution, leading to the facile electrodeposition of uniform the polymer film with outstanding electroactivity. Compared with poly(2′-aminomethyl- 3,4-ethylenedioxythiophene) (PEDOT-MeNH2), poly(2′-aminomethyl-3,4-ethylenedioxythiophene salt) (PEDOT-MeNH3 +A-) revealed higher efficiencies (156 cm2 C-1), lower bandgap (1.68 eV), and faster response time (1.4 s). Satisfactory results implied that salinization can not only change the polymerization system, but also adjust the optical absorption, thereby increase the electrochromic properties

Private and Flexible Urban Message Delivery

With the popularity of intelligent mobile devices, enormous amounts of urban information has been generated and demanded by the public. In response, ShanghaiGrid (SG) aims to provide abundant information services to the public. With a fixed schedule and urbanwide coverage, an appealing service in SG is to provide free message delivery service to the public using buses, which allows mobile device users to send messages to locations of interest via buses. The main challenge in realizing this service is to provide an efficient routing scheme with privacy preservation under a highly dynamic urban traffic condition. In this paper, we present the innovative scheme BusCast to tackle this problem. In BusCast, buses can pick up and forward personal messages to their destination locations in a store-carry-forward fashion. For each message, BusCast conservatively associates a routing graph rather than a fixed routing path with the message to adapt the dynamic of urban traffic. Meanwhile, the privacy information about the user and the message destination is concealed from both intermediate relay buses and outside adversaries. Both rigorous privacy analysis and extensive trace-driven simulations demonstrate the efficacy of the BusCast scheme