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    New Machine Learning Findings from University of New Hampshire Described (Multi- criteria Evaluation of Health News Stories)

    19-20页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting originating from Durham, Ne w Hampshire, by NewsRx correspondents, research stated, “The proliferation of di gital and social media technologies has enabled quick and wide dissemination of news stories and press releases about new medical treatments. Evaluating these s tories is difficult for two reasons.” Our news editors obtained a quote from the research from the University of New H ampshire, “First, these stories are often not completely true or false. A nuance d approach that considers different aspects of these stories (e.g., the presence of inflated claims, suppression of risks associated with the treatment or withh olding other essential information) is more appropriate for evaluation. Second, evaluating the quality and completeness of the arguments in the stories is costl y and requires expertise in the relevant medical field, which laypeople do not h ave. To address this problem, in this study, we train different machine learning models on multi-criteria expert evaluations for health news stories about new m edical treatments and compare their performance. We then compare the machine lea rning model evaluations to laypeople evaluations. We find that machine learning models overall outperform laypeople, who have a propensity to overestimate the c omprehensiveness of the claims. Our machine learning models employ multi-criteri a evaluation, which is different from most previous studies that evaluate news s tories on whether they are true or false.”
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    Findings from Tiangong University Provide New Insights into Robotics (Trajectory Tracking Control of Car-like Mobile Robots Based on Extended State Observer and Backstepping Control)

    20-20页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in robotics. According to news reporting from Tianjin, People’s Republic of China, by NewsRx journalists, research stated, “In this paper, a trajectory tracking co ntrol strategy for low-speed car-like mobile robots (CLMRs) based on an extended state observer (ESO) and backstepping control is proposed to address the issue of trajectory tracking accuracy degradation caused by modeling errors and extern al disturbances.” Financial supporters for this research include National Natural Science Foundati on of China; Tianjin Natural Science Foundation.
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    Data on Arthroplasty Reported by S. V. Gowtam and Colleagues (Accuracy of Femora l Component External Rotation with all Burr Robotic Assisted Total Knee Arthropl asty)

    21-21页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Surgery - Arthroplasty is the subject of a report. According to news reporting out of Nagpur, India, b y NewsRx editors, research stated, “External rotation of femoral component is co ntroversial in Total knee arthroplasty (TKA). The aim of our study is to assess the precision of femoral component external rotation in Robotic Assisted All Bur r TKA.” Our news journalists obtained a quote from the research, “This is a prospective study of 30 cases who underwent All Burr Robotic Assisted TKA at our institute, RNH hospital. Inclusion criteria were primary and secondary osteoarthritis of th e knee and exclusion criteria were revision and partial knee replacement. On Nav io robotic system femoral external rotation was planned as per medio-lateral fle xion gap balancing and executed with burr. Post-operative CT scan was done in al l patients to assess intra-operative planned femoral external rotation. Out of 3 0 cases, 20 were female and 10 were male. Mean age was 66.06±7.43 years. On Navi o the planned external rotation of femoral component was 2.86±1.16. Average of f emoral component external rotation on postoperative CT scan was 3.11±1.16. The m ean deviation of achieved femoral component external rotation from planned exter nal rotation was -0.24 to ±0.28. Only 37% patients required 3° ext ernal rotation. Correlation between planned and achieved femoral component exter nal rotation was significant, positive and very strong as indicated by r=0.97 an d p=0.0001.”
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    Studies in the Area of Robotics Reported from European Organization for Nuclear Research (CERN) (Modelling and Identification Methods for Simulation of Cable-su spended Dual-arm Robotic Systems)

    21-22页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Robotics is the subjec t of a report. According to news reporting originating in Meyrin, Switzerland, b y NewsRx journalists, research stated, “This paper proposes rigid - body modellin g and identification procedures for long -reach dual -arm manipulators in a cabl e -suspended pendulum configuration. The proposed model relies on a virtually co nstrained open kinematic chain and lends itself to be simulated through the most commonly used robotic simulators without explicitly account for the cables cons traints and flexibility.” Financial support for this research came from AERIAL-CORE project (Horizon 2020) .
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    Data on Machine Learning Detailed by Researchers at Charles Darwin University (A n Image Quality Assessment Method Based On Edge Extraction and Singular Value fo r Blurriness)

    22-23页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news reporting out of Darwin, Australia, by NewsRx editors, research stated, “The automatic assessment of perceived image quality is crucial in the field of image processing. To achieve this idea, we propose an image quality assessment (IQA) method for blurriness.” Financial support for this research came from Charles Darwin University.
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    Shanghai Jiao Tong University School of Medicine Reports Findings in Artificial Intelligence (Leveraging Large Language Models for Improved Patient Access and S elf-Management: Assessor-Blinded Comparison Between Expert- and AI-Generated Con tent)

    23-24页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news reporting out of Shanghai, Peopl e’s Republic of China, by NewsRx editors, research stated, “While large language models (LLMs) such as ChatGPT and Google Bard have shown significant promise in various fields, their broader impact on enhancing patient health care access an d quality, particularly in specialized domains such as oral health, requires com prehensive evaluation. This study aims to assess the effectiveness of Google Bar d, ChatGPT-3.5, and ChatGPT-4 in offering recommendations for common oral health issues, benchmarked against responses from human dental experts.” Our news journalists obtained a quote from the research from the Shanghai Jiao T ong University School of Medicine, “This comparative analysis used 40 questions derived from patient surveys on prevalent oral diseases, which were executed in a simulated clinical environment. Responses, obtained from both human experts an d LLMs, were subject to a blinded evaluation process by experienced dentists and lay users, focusing on readability, appropriateness, harmlessness, comprehensiv eness, intent capture, and helpfulness. Additionally, the stability of artificia l intelligence responses was also assessed by submitting each question 3 times u nder consistent conditions. Google Bard excelled in readability but lagged in ap propriateness when compared to human experts (mean 8.51, SD 0.37 vs mean 9.60, S D 0.33; P=.03). ChatGPT-3.5 and ChatGPT-4, however, performed comparably with hu man experts in terms of appropriateness (mean 8.96, SD 0.35 and mean 9.34, SD 0. 47, respectively), with ChatGPT-4 demonstrating the highest stability and reliab ility. Furthermore, all 3 LLMs received superior harmlessness scores comparable to human experts, with lay users finding minimal differences in helpfulness and intent capture between the artificial intelligence models and human responses. L LMs, particularly ChatGPT-4, show potential in oral health care, providing patie nt-centric information for enhancing patient education and clinical care. The ob served performance variations underscore the need for ongoing refinement and eth ical considerations in health care settings.”
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    New Artificial Intelligence Findings from University of South Carolina Reported [Neurosymbolic Value-inspired Artificial Intelligence (Why, W hat, and How)]

    24-25页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ar tificial Intelligence. According to news reporting originating from Columbia, So uth Carolina, by NewsRx correspondents, research stated, “The rapid progression of artificial intelligence (AI) systems, facilitated by the advent of large lang uage models (LLMs), has resulted in their widespread application to provide huma n assistance across diverse industries. This trend has sparked significant disco urse centered around the ever-increasing need for LLM-based AI systems to functi on among humans as a part of human society.” Financial support for this research came from National Science Foundation (NSF).
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    Chongqing University Reports Findings in Machine Learning (Functional near-infra red spectroscopy-based diagnosis support system for distinguishing between mild and severe depression using machine learning approaches)

    25-26页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating from Chongqing, P eople’s Republic of China, by NewsRx correspondents, research stated, “Early dia gnosis of depression is crucial for effective treatment. Our study utilizes func tional near-infrared spectroscopy (fNIRS) and machine learning to accurately cla ssify mild and severe depression, providing an objective auxiliary diagnostic to ol for mental health workers.” Our news editors obtained a quote from the research from Chongqing University, “ Develop prediction models to distinguish between severe and mild depression usin g fNIRS data. We collected the fNIRS data from 140 subjects and applied a comple te ensemble empirical mode decomposition with an adaptive noise-wavelet threshol d combined denoising method (CEEMDAN-WPT) to remove noise during the verbal flue ncy task. The temporal features (TF) and correlation features (CF) from 18 prefr ontal lobe channels of subjects were extracted as predictors. Using recursive fe ature elimination with cross-validation, we identified optimal TF or CF and exam ined their role in distinguishing between severe and mild depression. Machine le arning algorithms were used for classification. The combination of TF and CF as inputs for the prediction model yielded higher classification accuracy than usin g either TF or CF alone. Among the prediction models, the SVM-based model demons trates excellent performance in nested cross-validation, achieving an accuracy r ate of 92.8%.”
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    Stanford University Reports Findings in Machine Learning (Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance)

    26-27页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting from Stanford, California, by NewsRx journalists, research stated, “Computationally predicting the performanc e of catalysts under reaction conditions is a challenging task due to the comple xity of catalytic surfaces and their evolution in situ, different reaction paths , and the presence of solid-liquid interfaces in the case of electrochemistry. W e demonstrate here how relatively simple machine learning models can be found th at enable prediction of experimentally observed onset potentials.” The news correspondents obtained a quote from the research from Stanford Univers ity, “Inputs to our model are comprised of data from the oxygen reduction reacti on on non-precious transition-metal antimony oxide nanoparticulate catalysts wit h a combination of experimental conditions and computationally affordable bulk a tomic and electronic structural descriptors from density functional theory simul ations. From human-interpretable genetic programming models, we identify key exp erimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. We finally v alidate these machine learning predictions by experimentally confirming that sca ndium as a dopant in nickel antimony oxide leads to a desired onset potential in crease.”
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    Researchers from Sichuan University Report Details of New Studies and Findings i n the Area of Machine Learning (An Accelerated Stochastic Admm for Nonconvex and Nonsmooth Finite-sum Optimization)

    27-28页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning is now available. According to news reporting from Chengdu, People’s Republic of China , by NewsRx journalists, research stated, “The nonconvex and nonsmooth finite -s um optimization problem with linear constraint has attracted much attention in t he fields of artificial intelligence, computer, and mathematics, due to its wide applications in machine learning and the lack of efficient algorithms with conv incing convergence theories. A popular approach to solve it is the stochastic Al ternating Direction Method of Multipliers (ADMM), but most stochastic ADMM-type methods focus on convex models.” Funders for this research include National Key Research and Development Program of China, National Natural Science Foundation of China (NSFC), Natural Science F oundation of Sichuan Province, China, Guangdong Basic and Applied Basic Research Foundation, China, Shaanxi Fundamental Science Research Project for Mathematics and Physics, China, Sichuan Youth Science and Technology Innovation Team, China .
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