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Robotics & Machine Learning Daily News

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    Reports from Los Alamos National Laboratory Highlight Recent Findings in Machine Learning (Characterizing Different Motilityinduced Regimes In Active Matter With Machine Learning and Noise)

    20-21页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news originating from Los Alamos, New Mexico, by NewsRx correspondents, research stated, “We examine motility-induced phase separation (MIPS) in two-dimensional run-and-tumble disk systems using both machine learning and noise fluctuation analysis. Our measures suggest that within the MIPS state there are several distinct regimes as a function of density and run time, so that systems with MIPS transitions exhibit an active fluid, an active crystal, and a critical regime.” Financial supporters for this research include U.S. Department of Energy through the Los Alamos National Laboratory, United States Department of Energy (DOE), M. J. Murdock Charitable Trust. Our news journalists obtained a quote from the research from Los Alamos National Laboratory, “The different regimes can be detected by combining an order parameter extracted from principal component analysis with a cluster stability measurement. The principal component-derived order parameter is maximized in the critical regime, remains low in the active fluid, and has an intermediate value in the active crystal regime. We demonstrate that machine learning can better capture dynamical properties of the MIPS regimes compared to more standard structural measures such as the maximum cluster size. The different regimes can also be characterized via changes in the noise power of the fluctuations in the average speed.”
      原文链接:
    • NETL
    • NSTL

    Reports Summarize Machine Learning Study Results from Northern Border University (Machine Learning-assisted Prediction and Optimization of Exergy Efficiency and Destruction of Cumene Plant under Uncertainty)

    21-21页
    查看更多>>摘要：Fresh data on artificial intelligence are presented in a new report. According to news originating from Northern Border University by NewsRx correspondents, research stated, “Machine Learning (ML)’s growing role in process industries during the digitalization era is notable.” Our news journalists obtained a quote from the research from Northern Border University: “This study combines Artificial Neural Networks (ANNs) and Aspen Plus to predict exergy efficiency, exergy destruction, and potential improvements in a cumene plant under uncertain process conditions. An optimization framework, using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), was developed to enhance exergy efficiency amid uncertainty. Initially, a steady-state Aspen model evaluates exergy efficiency, irreversibility, and potential improvements. The proposed model is transitioned to a dynamic mode, introducing artificial uncertainties into key variables. An ANN model predicts exergy efficiency and exergy destruction under uncertainty. The PSO and GA-based optimization methods improve exergy efficiency and reduce exergy destruction.”
      原文链接:
    • NETL
    • NSTL
    • Eos Assoc

    Center for Advanced Systems Understanding (CASUS) Reports Findings in Artificial Intelligence (Toward the novel AI tasks in infection biology)

    22-23页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting out of Gorlitz, Germany, by NewsRx editors, the research stated, “Machine learning and artificial intelligence (AI) are becoming more common in infection biology laboratories around the world. Yet, as they gain traction in research, novel frontiers arise.” Our news journalists obtained a quote from the research from Center for Advanced Systems Understanding (CASUS), “Novel artificial intelligence algorithms are capable of addressing advanced tasks like image generation and question answering. However, similar algorithms can prove useful in addressing advanced questions in infection biology like prediction of host-pathogen interactions or inferring virus protein conformations. Addressing such tasks requires large annotated data sets, which are often scarce in biomedical research. In this review, I bring together several successful examples where such tasks were addressed. I underline the importance of formulating novel AI tasks in infection biology accompanied by freely available benchmark data sets to address these tasks. Furthermore, I discuss the current state of the field and potential future trends.”
      原文链接:
    • NETL
    • NSTL

    New Machine Learning Research Has Been Reported by Researchers at University of Calgary (Machine Learning Aids Rapid Assessment of Aftershocks: Application to the 2022-2023 Peace River Earthquake Sequence, Alberta, Canada)

    22-22页
    查看更多>>摘要：Data detailed on artificial intelligence have been presented. According to news reporting out of Calgary, Canada, by NewsRx editors, research stated, “The adoption of machine learning (ML) models has ignited a paradigm shift in seismic analysis, fostering enhanced efficiency in capturing patterns of seismic activity with reduced need for time-consuming user interaction.” Our news reporters obtained a quote from the research from University of Calgary: “Here, we investigate automated event detection and extraction of seismic phases using two widely used ML models: EQTransformer and PhaseNet. We applied both the models to four weeks of continuous recordings of aftershocks using a temporary array following the 30 November 2022, ML 5.6 earthquake near Peace River, Alberta, Canada. Both the tools identified ＞1000 events over the recording period. The aftershocks are located in close proximity to the ML 5.6 mainshock as well as to wastewater disposal operations that were ongoing at the time. Both the methods reveal an aftershock distribution that was not identified by the regional network; however, we find that events detected by PhaseNet have smaller event location errors and better depict subtle fault structures at depth, despite identifying 200 events less than EQTransformer.”
      原文链接:
    • NETL
    • NSTL

    Albert Einstein College of Medicine Reports Findings in Subarachnoid Hemorrhage (Prediction of delayed cerebral ischemia followed aneurysmal subarachnoid hemorrhage. A machine-learning based study)

    23-24页
    查看更多>>摘要：New research on Central Nervous System Diseases and Conditions - Subarachnoid Hemorrhage is the subject of a report. According to news reporting originating in Bronx, New York, by NewsRx journalists, research stated, “Delayed Cerebral Ischemia (DCI) is a significant complication following aneurysmal subarachnoid hemorrhage (aSAH) that can lead to poor outcomes. Machine learning techniques have shown promise in predicting DCI and improving risk stratification.” The news reporters obtained a quote from the research from the Albert Einstein College of Medicine, “In this study, we aimed to develop machine learning models to predict the occurrence of DCI in patients with aSAH. Patient data, including various clinical variables and co-factors, were collected. Six different machine learning models, including logistic regression, multilayer perceptron, decision tree, random forest, gradient boosting machine, and extreme gradient boosting (XGB), were trained and evaluated using performance metrics such as accuracy, area under the curve (AUC), precision, recall, and F1 score. After data augmentation, the random forest model demonstrated the best performance, with an AUC of 0.85. The multilayer perceptron neural network model achieved an accuracy of 0.93 and an F1 score of 0.85, making it the best performing model. The presence of positive clinical vasospasm was identified as the most important feature for predicting DCI. Our study highlights the potential of machine learning models in predicting the occurrence of DCI in patients with aSAH. The multilayer perceptron model showed excellent performance, indicating its utility in risk stratification and clinical decision-making. However, further validation and refinement of the models are necessary to ensure their generalizability and applicability in real-world settings.”
      原文链接:
    • NETL
    • NSTL

    Shanghai Jiao Tong University School of Medicine Reports Findings in Lung Cancer (Meta-lasso: new insight on infection prediction after minimally invasive surgery)

    24-25页
    查看更多>>摘要：New research on Oncology - Lung Cancer is the subject of a report. According to news reporting out of Shanghai, People’s Republic of China, by NewsRx editors, research stated, “Surgical site infection (SSI) after minimally invasive lung cancer surgery constitutes an important factor influencing the direct and indirect economic implications, patient prognosis, and the 5-year survival rate for early-stage lung cancer patients. In the realm of predictive healthcare, machine learning algorithms have been instrumental in anticipating various surgical outcomes, including SSI.” Our news journalists obtained a quote from the research from the Shanghai Jiao Tong University School of Medicine, “However, accurately predicting infection after minimally invasive surgery remains a clinical challenge due to the multitude of physiological and surgical factors associated with it. Furthermore, clinical patient data, in addition to being high-dimensional, often exists the long-tail problem, posing difficulties for traditional machine learning algorithms in effectively processing such data. Based on this insight, we propose a novel approach called meta-lasso for infection prediction following minimally invasive surgery. Our approach leverages the sparse learning algorithm lasso regression to select informative features and introduces a meta-learning framework to mitigate bias towards the dominant class. We conducted a retrospective cohort study on patients who had undergone minimally invasive surgery for lung cancer at Shanghai Chest Hospital between 2018 and 2020. The evaluation encompassed key performance metrics, including sensitivity, specificity, precision (PPV), negative predictive value (NPV), and accuracy.”
      原文链接:
    • NETL
    • NSTL

    Reports Summarize Robotics Findings from Zhejiang University of Technology (A Learning Based Hierarchical Control Framework for Human-robot Collaboration)

    25-26页
    查看更多>>摘要：Current study results on Robotics have been published. According to news reporting originating from Hangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “In this paper, using the ball and beam system as an illustration, a control scheme is developed on humanrobot collaboration, i.e., a two-level hierarchical framework is proposed to establish a robust human-robot collaboration (HRC) policy. On the high level, a deep reinforcement learning (DRL) algorithm is presented to plan the desired beam rotational velocity.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Natural Science Foundation of Zhejiang Province, Fundamental Research Funds for the Provincial Universities of Zhejiang. Our news editors obtained a quote from the research from the Zhejiang University of Technology, “The low level is constructed by a human-intention perception module and a robust collaboration policy design module. For the first module, a probabilistic model is fitted by using the Gaussian process regression (GPR) approach to predict human-hand velocities, and prediction results follow Gaussian distributions where mean values and variances represent predicted human-hand velocities and corresponding prediction confidences, respectively. For the second module, a robust collaboration policy is established by fusing a proactive policy and a conservative policy, where the proactive policy is used to control the robot to achieve the desired beam rotational velocity by using the predicted human-hand velocities. The conservative policy is designed to ensure the collaboration safety. The weighted parameters for fusion are adaptively tuned based on the prediction precision and confidence.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Data on Turing Machines Described by Researchers at Chinese Academy of Sciences (Turing Machines With Two-level Memory: New Computational Models for Analyzing the Input/output Complexity)

    26-27页
    查看更多>>摘要：Current study results on Turing Machines have been published. According to news reporting originating in Shenzhen, People’s Republic of China, by NewsRx journalists, research stated, “The input/output complexity, which is the complexity of data exchange between the main memory and the external memory, has been elaborately studied by a lot of former researchers. However, the existing works failed to consider the input/output complexity in a computational model point of view.” Funders for this research include National Natural Science Foundation of China (NSFC), National Key Research and Development Program of China. The news reporters obtained a quote from the research from the Chinese Academy of Sciences, “In this paper we remedy this by proposing four variants of Turing machine that include external memory and the mechanism of exchanging data between main memory and external memory. Based on these new models, the input/output complexity is deeply studied. We discuss the relationship between input/output complexity and the other complexity measures such as time complexity and parameterized complexity, which is not considered by former researchers. We also define the external access trace complexity, which reflects the physical behavior of magnetic disks and gives a theoretical evidence of IO-efficient algorithms.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Researchers from Northeastern University Report on Findings in Robotics (How Do Robot Touch Characteristics Impact Users’ Emotional Responses: Evidence From Ecg and Fnirs)

    27-28页
    查看更多>>摘要：Investigators discuss new findings in Robotics. According to news reporting out of Shenyang, People’s Republic of China, by NewsRx editors, research stated, “Robot touch is a vital interaction mode for emotional communication and human mental support in HRI. However, little is known about how robot touch characteristics influence the users’ subjective perception of emotion and physiological reactions.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Northeastern University, “Therefore, a within-subject experiment of robot touches was conducted with the touch type (contact versus grip), length (long versus short), and location (hand versus forearm) as independent variables. The subjective perception was measured with PANAS scales. Electrocardiography (ECG) and functional near-infrared spectroscopy (fNIRS) were utilized to measure the participant’s cardiac autonomic nervous system responses and cerebral central nervous system responses. Results showed that touch type and length jointly affected users’ subjective perception of emotion and cerebral activity, and location affected users’ heart rate variability and cerebral activity. The results suggest that robot short-grip and long-contact behaviors might bring users more positive emotions.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Reports from Harbin Institute of Technology Provide New Insights into Robotics (Push Recovery Control Based On Model Predictive Control of Hydraulic Quadruped Robots)

    28-29页
    查看更多>>摘要：Investigators publish new report on Robotics. According to news reporting originating in Harbin, People’s Republic of China, by NewsRx journalists, research stated, “This work is aimed at addressing the balance problem of hydraulic quadruped robots, trotting on even terrain, which is impacted by lateral disturbance. The ability of push recovery means that a robot can restore stability when the roll angle of the body is too large after a strong side impact.” The news reporters obtained a quote from the research from the Harbin Institute of Technology, “To maintain the balance of the impacted robot, three strategies are proposed inspired by the human response to external disturbance, including supporting leg adjustment strategy, one-step motion of swinging legs strategy, and N-step motion of swinging legs strategy. Quadruped robots can be considered as humanoids owing to the nature of their trotting gait. Thus, the contributions of this artile are as follows. A simplified dynamic model of a quadruped robot is established based on linear inverted pendulum (LIP), and the idea of capture point (CP) and zero moment point (ZMP). A push recovery control system based on model predictive controller (MPC) is established according to the requirement of the control strategy.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature