查看更多>>摘要：Research findings on Artificial Intelligence are discussed in a new report. According to news reporting from Tucson, Arizona, by NewsRx editors, the research stated, “As artificial intelligence (AI) expands its presence in healthcare, particularly within emergency medicine (EM), there is growing urgency to explore the ethical and practical considerations surrounding its adoption. AI holds the potential to revolutionize how emergency physicians (EPs) make clinical decisions, but AI’s complexity often surpasses EPs’ capacity to provide patients with informed consent regarding its use.” The news correspondents obtained a quote from the research from the University of Arizona, “This article underscores the crucial need to address the ethical pitfalls of AI in EM. Patient autonomy necessitates that EPs engage in conversations with patients about whether to use AI in their evaluation and treatment. As clinical AI integration expands, this discussion should become an integral part of the informed consent process, aligning with ethical and legal requirements.The rapid availability of AI programs, fueled by vast electronic health record (EHR) datasets, has led to increased pressure on hospitals and clinicians to embrace clinical AI without comprehensive system evaluation. However, the evolving landscape of AI technology outpaces our ability to anticipate its impact on medical practice and patient care. The central question arises: Are EPs equipped with the necessary knowledge to offer well-informed consent regarding clinical AI? Collaborative efforts between EPs, bioethicists, AI researchers, and healthcare administrators are essential for the development and implementation of optimal AI practices in EM. To facilitate informed consent about AI, EPs should understand at least seven key areas: (1) how AI systems operate; (2) whether AI systems are understandable and trustworthy; (3) the limitations of and errors AI systems make; (4) how disagreements between the EP and AI are resolved; (5) whether the patient’s personally identifiable information (PII) and the AI computer systems will be secure; (6) if the AI system functions reliably (has been validated); and (7) if the AI program exhibits bias.”
查看更多>>摘要：Research findings on artificial intelligence are discussed in a new report. According to news reporting from Edwardsville, Illinois, by NewsRx journalists, research stated, “This study presents the application of machine learning (ML) to evaluate marine fog visibility conditions and nowcasting of visibility based on the FATIMA (Fog and turbulence interactions in the marine atmosphere) campaign observations collected during July 2022 in the North Atlantic in the Grand Banks area and vicinity of Sable Island, northeast of Canada. The measurements were collected using instrumentation mounted on the Research Vessel Atlantic Condor.” Financial supporters for this research include Office of Naval Research.
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating from Chengdu, People’s Republic of China, by NewsRx correspondents, research stated, “In recent decades, more than 100,000 scientific articles have been devoted to the development of electrode materials for supercapacitors and batteries. However, there is still intense debate surrounding the criteria for determining the electrochemical behavior involved in Faradaic reactions, as the issue is often complicated by the electrochemical signals produced by various electrode materials and their different physicochemical properties.”
查看更多>>摘要：New research on artificial intelligence is the subject of a new report. According to news reporting originating from Kongens Lyngby, Denmark, by NewsRx correspondents, research stated, “With advancements in both the quality and collection speed of magnetic data captured by uncrewed aerial vehicle (UAV)-based systems, there is a growing need for robust and efficient systems to automatically interpret such data.” Financial supporters for this research include Technical University of Denmark Discovery Grant. Our news journalists obtained a quote from the research from Technical University of Denmark (DTU): “Many existing conventional methods require manual inspection of the survey data to pick out candidate areas for further analysis. We automate this initial process by implementing unsupervised machine learning techniques to identify small, well-defined regions. When further analysis is conducted with magnetic inversion algorithms, then our approach also reduces the nonlinear computation and time costs by breaking one huge inversion problem into several smaller ones. We also demonstrate robustness to noise and sidestep the requirement for large quantities of labeled training data: two pitfalls of current automation approaches. We propose first to use hierarchical clustering on filtered magnetic gradient data and then to fit ellipses to the resulting clusters to identify subregions for further analysis.”
查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting originating from Shanghai, People’s Republic of China, by NewsRx correspondents, research stated, “We investigate the factor investing in Chinese commodities markets following two steps. The first step is to find profitable characteristics.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from Shanghai Jiao Tong University, “We find that some technical characteristics can produce a comparable out-of-sample performance to the fundamental characteristics. The second step is to integrate various commodity characteristics to generate a composite signal. We apply the naive equal-weighted model, three linear models and four tree-ensemble nonlinear models for style integration. The empirical results show that the four nonlinear machine learning integration models produce better out-of-sample performance than the linear models.”
查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting originating in Chennai, India, by NewsRx journalists, research stated, “Innovations in deep learning technology have recently focused on photonics as a computing medium. Integrating an electronic and photonic approach is the main focus of this work utilizing various photonic architectures for machine learning applications.” Funders for this research include Siemens AG, MathWorks, Inc. The news reporters obtained a quote from the research from the Department of Electrical and Communication Engineering, “The speed, power, and reduced footprint of these photonic hardware accelerators (HA) are expected to greatly enhance inference. In this work, we propose a hybrid design of an electronic and photonic integrated circuit (EPIC) hardware accelerator (EPICHA), an electronic-photonic framework that uses architecture-level integrations for better performance. The proposed EPICHA has a systematic structure of reconfigurable directional couplers (RDCs) to build a scalable, efficient machine learning accelerator for inference applications. In the simulation framework, the input and output layers of a fully integrated photonic neural network use the same integrated photodetector and RDC technology as the activation function. Our system only compromises on latency because of the electro-optical conversion process and the hand-off between the electronic and photonic domains. Insertion losses in photonic components have a small negative impact on accuracy when using more deep learning stages. Our proposed EPICHA utilizes coherent operation, and hence uses a single wavelength of lambda = 1550 nm. We used the interoperability feature of the Ansys Lumerical MODE, DEVICE, and INTERCONNECT tools for component modeling in the photonic and electrical domain, and circuit-level simulation using S-parameters with MATLAB. The electronic component acts as the controller, which generates the required analog voltage control signals for each RDC present in the photonic processing engine.”
查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news originating from Valladolid, Spain, by NewsRx correspondents, research stated, “Motor imagery (MI) based brain-computer interfaces (BCIs) are widely used in rehabilitation due to the close relationship that exists between MI and motor execution (ME). However, the underlying brain mechanisms of MI remain not well understood.” Our news journalists obtained a quote from the research from the University of Valladolid, “Most MIBCIs use the sensorimotor rhythms elicited in the primary motor cortex (M1) and somatosensory cortex (S1), which consist of an event-related desynchronization followed by an event-related synchronization. Consequently, this has resulted in systems that only record signals around M1 and S1. However, MI could involve a more complex network including sensory, association, and motor areas. In this study, we hypothesize that the superior accuracies achieved by new deep learning (DL) models applied to MI decoding rely on focusing on a broader MI activation of the brain. Parallel to the success of DL, the field of explainable artificial intelligence (XAI) has seen continuous development to provide explanations for DL networks success. The goal of this study is to use XAI in combination with DL to extract information about MI brain activation patterns from non-invasive electroencephalography (EEG) signals. We applied an adaptation of Shapley additive explanations (SHAP) to EEGSym, a state-of-the-art DL network with exceptional transfer learning capabilities for inter-subject MI classification. We obtained the SHAP values from two public databases comprising 171 users generating left and right hand MI instances with and without real-time feedback. We found that EEGSym based most of its prediction on the signal of the frontal electrodes, i.e. F7 and F8, and on the first 1500 ms of the analyzed imagination period. We also found that MI involves a broad network not only based on M1 and S1, but also on the prefrontal cortex (PFC) and the posterior parietal cortex (PPC). We further applied this knowledge to select a 8-electrode configuration that reached inter-subject accuracies of 86.5% ± 10.6% on the Physionet dataset and 88.7% ± 7.0% on the Carnegie Mellon University’s dataset. Our results demonstrate the potential of combining DL and SHAP-based XAI to unravel the brain network involved in producing MI.”
查看更多>>摘要：Current study results on Artificial Intelligence have been published. According to news reporting from Bristol, United Kingdom, by NewsRx editors, the research stated, “The paper estimates whether there is any relationship between life satisfaction and people’s perceptions towards artificial intelligence.” The news correspondents obtained a quote from the research from the University of the West of England, “Using data from 39 European countries collected in 2021, it is consistently found that people with negative perceptions report lower life satisfaction. This finding is robust across a number of robustness checks.” According to the news reporters, the research concluded: “This provides further evidence that people may fear some new technologies, in this case artificial intelligence, which adds weight to governments needing to establish moratoriums to openly discuss what the objectives of new science, technologies and innovations are and how best to manage and steer policy and regulation to achieve these objectives.” This research has been peer-reviewed.
查看更多>>摘要：New research on Immunization - Vaccines is the subject of a report. According to news reporting originating from Shaanxi, People’s Republic of China, by NewsRx correspondents, research stated, “Seasonal influenza A H3N2 viruses are constantly changing, reducing the effectiveness of existing vaccines. As a result, the World Health Organization (WHO) needs to frequently update the vaccine strains to match the antigenicity of emerged H3N2 variants.” Our news editors obtained a quote from the research from Northwestern Polytechnical University, “Traditional assessments of antigenicity rely on serological methods, which are both labor-intensive and time-consuming. Although numerous computational models aim to simplify antigenicity determination, they either lack a robust quantitative linkage between antigenicity and viral sequences or focus restrictively on selected features. Here, we propose a novel computational method to predict antigenic distances using multiple features, including not only viral sequence attributes but also integrating four distinct categories of features that significantly affect viral antigenicity in sequences. This method exhibits low error in virus antigenicity prediction and achieves superior accuracy in discerning antigenic drift. Utilizing this method, we investigated the evolution process of the H3N2 influenza viruses and identified a total of 21 major antigenic clusters from 1968 to 2022. Interestingly, our predicted antigenic map aligns closely with the antigenic map generated with serological data.”
查看更多>>摘要：Investigators publish new report on Robotics. According to news reporting out of Wuhan, People’s Republic of China, by NewsRx editors, research stated, “Bag manipulation through robots is complex and challenging due to the deformability of the bag. Based on the dynamic manipulation strategy, we propose a new framework, ShakingBot, for the bagging tasks.” Financial supporters for this research include Project, Research on Video Tracking Based on Manifold Statistical Analysis, Key Project of the Science and Technology Research Program of the Hubei Provincial Department of Education. Our news journalists obtained a quote from the research from Wuhan Textile University, “ShakingBot utilizes a perception module to identify the key region of the plastic bag from arbitrary initial configurations. According to the segmentation, ShakingBot iteratively executes a novel set of actions, including Bag Adjustment, Dual-arm Shaking, and One-arm Holding, to open the bag. The dynamic action, Dual-arm Shaking, can effectively open the bag without the need to take into account the crumpled configuration. Then, the robot inserts the items and lifts the bag for transport. We perform our method on a dualarm robot and achieve a success rate of 21/33 for inserting at least one item across various initial bag configurations. In this work, we demonstrate the performance of dynamic shaking action compared to the quasi-static manipulation in the bagging task.”
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