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    Reports Outline Machine Translation Study Results from Minzu University of China (Unsupervised Multilingual Machine Translation With Pretrained Cross-lingual Encoders)

    38-38页
    查看更多>>摘要：Data detailed on Machine Translation have been presented. According to news originating from Beijing, People's Republic of China, by NewsRx correspondents, research stated, “Multilingual Neural Machine Translation (MNMT) has recently made great progress in training models that can translate between multiple languages. However, MNMT faces a significant challenge due to the lack of sufficient parallel corpora for all language pairs.” Financial supporters for this research include Chinese National Funding of Social Sciences, National Language Commis-sion Foundation of China. Our news journalists obtained a quote from the research from the Minzu University of China, “Unsupervised machine translation methods, which utilize monolingual data, have emerged as a solution to this challenge. In this paper, we propose a method that leverages cross-lingual encoders, such as XLM-R, in an unsupervised manner (i.e., using monolingual data and bilingual dictionaries) to train a MNMT model. Our method initializes the MNMT model with a pre-trained cross-lingual encoder and employs two levels of alignment to further align the representation space in MNMT model.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    National Technical University of Athens Reports Findings in Artificial Intelligence (Enhancing Internet of Medical Things security with artificial intelligence: A comprehensive review)

    38-39页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting out of Athens, Greece, by NewsRx editors, research stated, “Over the past five years, interest in the literature regarding the security of the Internet of Medical Things (IoMT) has increased. Due to the enhanced interconnectedness of IoMT devices, their susceptibility to cyber-attacks has proportionally escalated.” Our news journalists obtained a quote from the research from the National Technical University of Athens, “Motivated by the promising potential of AI-related technologies to improve certain cybersecurity measures, we present a comprehensive review of this emerging field. In this review, we attempt to bridge the corresponding literature gap regarding modern cybersecurity technologies that deploy AI techniques to improve their performance and compensate for security and privacy vulnerabilities. In this direction, we have systematically gathered and classified the extensive research on this topic. Our findings highlight the fact that the integration of machine learning (ML) and deep learning (DL) techniques improves both the performance of cybersecurity measures and their speed, reliability, and effectiveness. This may be proven to be useful for improving the security and privacy of IoMT devices. Furthermore, by considering the numerous advantages of AI technologies as opposed to their core cybersecurity counterparts, including blockchain, anomaly detection, homomorphic encryption, differential privacy, federated learning, and so on, we provide a structured overview of the current scientific trends.”
      原文链接:
    • NETL
    • NSTL

    Researchers from University of Tyumen Report New Studies and Findings in the Area of Machine Learning (Application of Machine Learning To Fischer-tropsch Synthesis for Cobalt Catalysts)

    39-40页
    查看更多>>摘要：A new study on Machine Learning is now available. According to news originating from Tyumen, Russia, by NewsRx correspondents, research stated, “Machine Learning was used to make a prediction model for six property parameters (COconv (%); CH4 (%); CO2 (%); C2-C4 (%); C5+ (%); and COconv*C5+ (%)) from 16 feature parameters of 169 Fischer-Tropsch synthesis experiments for cobalt catalyst. The Random Forest method was chosen as the 'black-box' prediction tool, and cross-validation tests revealed small mean average errors: 10.9 +/- 1.8; 3.7 +/- 0.7, 1.5 +/- 0.6, 3.6 +/- 0.8, 7.4 +/- 1.4 and 9.0 +/- 1.3, respectively.” Funders for this research include , University of Tyumen, Tyumen Oblast Government, as part of the West-Siberian Interregional Science and Education Center's project, University of Tyumen, Russian Science Foundation (RSF). Our news journalists obtained a quote from the research from the University of Tyumen, “The most important feature parameters were Brunauer-Emmett-Teller (BET) surface area, pressure (P), and temperature (T). The Decision Tree was chosen to build an explainable model, which revealed that BET in the range of [199.5, 529] leads to small COconv (%); C5+(%); and COcon*C5+(%) values. Outside this range, the samples tend to have large conversion and selectivity values. Most importantly, the BET effect is not linear or monotonic; it can be extracted using machine learning methods, and current work has shown how to use it. The resulting rules can be used for further experiments to maximize the efficiency and develop a new catalyst system.”
      原文链接:
    • NETL
    • NSTL
    • Amer Chemical Soc

    Researchers from University of British Columbia Describe Findings in Telemedicine (Capturing Complex Hand Movements and Object Interactions Using Machine Learning-powered Stretchable Smart Textile Gloves)

    40-41页
    查看更多>>摘要：Investigators publish new report on Telemedicine. According to news reporting originating from Vancouver, Canada, by NewsRx correspondents, research stated, “Accurate real-time tracking of dexterous hand movements has numerous applications in human-computer interaction, the metaverse, robotics and tele-health. Capturing realistic hand movements is challenging because of the large number of articulations and degrees of freedom.” Funders for this research include Natural Sciences and Engineering Research Council of Canada (NSERC), Canadian Institutes of Health Research (CIHR), NSERC Discovery (NSERC), Natural Sciences and Engineering Research Council of Canada (NSERC), Mitacs, Canada Foundation for Innovation. Our news editors obtained a quote from the research from the University of British Columbia, “Here we report accurate and dynamic tracking of articulated hand and finger movements using stretchable, washable smart gloves with embedded helical sensor yarns and inertial measurement units. The sensor yarns have a high dynamic range, responding to strains as low as 0.005% and as high as 155%, and show stability during extensive use and washing cycles. We use multi-stage machine learning to report average joint-angle estimation root mean square errors of 1.21 degrees and 1.45 degrees for intra- and interparticipant cross-validation, respectively, matching the accuracy of costly motion-capture cameras without occlusion or field-of-view limitations. We report a data augmentation technique that enhances robustness to noise and variations of sensors. We demonstrate accurate tracking of dexterous hand movements during object interactions, opening new avenues of applications, including accurate typing on a mock paper keyboard, recognition of complex dynamic and static gestures adapted from American Sign Language, and object identification. Accurate real-time tracking of dexterous hand movements and interactions has applications in human-computer interaction, the metaverse, robotics and tele-health. Capturing realistic hand movements is challenging due to the large number of articulations and degrees of freedom.”
      原文链接:
    • NETL
    • NSTL

    Researchers from Tongji University Describe Findings in Robotics (Hierarchical Task-oriented Whole-body Locomotion of a Walking Exoskeleton Using Adaptive Dynamic Motion Primitive for Cart Pushing)

    41-43页
    查看更多>>摘要：A new study on Robotics is now available. According to news reporting from Shanghai, People's Republic of China, by NewsRx journalists, research stated, “This paper proposes a hierarchical task-oriented whole-body locomotion framework for the exoskeleton walking-cart pushing (EWCP) task, which includes a straight gait and a bypassing gait, allowing the exoskeleton robot to avoid obstacles during walking. In this framework, the core components are gait planning and phase estimation for locomotion in unstructured environments.” Financial support for this research came from National Key Research and Development Program of China. The news correspondents obtained a quote from the research from Tongji University, “Notably, our mobile redundancy exoskeleton system can provide more flexibility and versatility in manipulation when performing complex tasks. For the hierarchical task-oriented whole-body locomotion, the detour gait consists of straight lines and turning shapes so that the EWCP system can avoid obstacles on the ground. For gait planning, we use the dynamic motion primitives to learn the joint motion trajectory of whole-body locomotion, which has good generalization ability and adaptability with respect to the gait. For phase estimation, the current gait phase can be estimated from the joint angles. Additionally, we design an task switching mechanism, where the exoskeleton system can switch different configurations flexibly for different scenarios, such as track switching only with both feet supported. And the phase estimation and gait switching strategy ensure the stability of task switching. The experimental results show that the exoskeleton can effectively accomplish EWCP tasks in an environment with obstacles. Our work has also shown that even some challenging motion tasks can be implemented with relatively simple controllers, which greatly simplifies the design of control systems. Note to Practitioners-This paper is motivated by issues of hierarchical tasks of the lower limb exoskeleton. Traditional exoskeletons cannot achieve obstacle avoidance in complex scenes because they do not have enough degrees of freedom or they do not use hierarchical locomotion. In this paper, a hierarchical task-oriented whole-body locomotion is proposed, which includes a straight gait and a bypassing gait, allowing the exoskeleton robot to avoid obstacles during walking. For gait planning, we use DMP to generate the gait trajectory, which can ensure the smoothness of trajectory. For phase estimation, the current gait phase can be estimated from the joint angles. Additionally, we design an task switching mechanism, where the exoskeleton system can switch different configurations flexibly for different scenarios, such as track switching only with both feet supported. And the phase estimation and gait switching strategy ensure the stability of task switching. The proposed hierarchical task-oriented whole-body locomotion framework is expected to be applied to exoskeletons to assist patients in rehabilitation training and users in mobility in daily life. Additionally, the proposed framework uses a series of motion primitives to learn and reproduce the trajectory and update it online, which requires a heavy computation load calculation to ensure the update speed of the trajectory.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Investigators at University of Virginia Detail Findings in Machine Learning (Modeling the Relationship Between Urban Tree Canopy, Landscape Heterogeneity, and Land Surface Temperature: a Machine Learning Approach)

    43-44页
    查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting from Charlottesville, Virginia, by NewsRx journalists, research stated, “Cities across the United States and around the globe are embracing urban greening as a strategy for mitigating the effects of rising temperatures on human health and quality-of-life. Better understanding how the spatial configuration of tree canopy influences land surface temperature should help to increase the positive impacts of urban greening.” The news correspondents obtained a quote from the research from the University of Virginia, “This study applies a machine learning approach for modeling the relationship between urban tree canopy, landscape heterogeneity, and land surface temperature (LST) using data from nine cities located in nine different climate zones of the United States. We collected summer LST data from the U.S. Geological Survey (USGS) Analysis Ready Data series and processed them to derive mean, minimum, and maximum LST in degrees Fahrenheit for each Census block group within the cities considered. We also calculated the percentage of each block group comprised by the land cover designations in the 2016 or 2019 National Land Cover Database (NLCD) maintained by the USGS, depending on the vintage of the available LST data. High resolution tree canopy data were purchased for all the study cities and the spatial configuration of tree canopy was measured at the block group level using established landscape metrics. Landscape metrics of the waterbodies were also calculated to incorporate the cooling effects of waterbodies. We used a Generalized Boosted Regression Model (GBM) algorithm to predict LST from the collected data. Our results show that tree canopy exerts a consistent and significant influence on predicted land surface temperatures across all study cities, but that the configuration of tree canopy and water patches matters more in some locations than in others.”
      原文链接:
    • NETL
    • NSTL
    • Sage

    South China University of Technology Reports Findings in Breast Cancer (Development of a machine learning-based radiomics signature for estimating breast cancer TME phenotypes and predicting anti-PD-1/PD-L1 immunotherapy response)

    44-45页
    查看更多>>摘要：New research on Oncology - Breast Cancer is the subject of a report. According to news reporting from Guangzhou, People's Republic of China, by NewsRx journalists, research stated, “Since breast cancer patients respond diversely to immunotherapy, there is an urgent need to explore novel biomarkers to precisely predict clinical responses and enhance therapeutic efficacy. The purpose of our present research was to construct and independently validate a biomarker of tumor microenvironment (TME) phenotypes via a machine learning-based radiomics way.” Funders for this research include Key-Area Research and Development Program of Guangdong Province, Regional Innovation and Development Joint Fund of National Natural Science Foundation of China, National Natural Science Foundation of China, Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, High-level Hospital Construction Project.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    New Machine Learning Data Have Been Reported by Researchers at Massachusetts Institute of Technology (Machine Learning From Quantum Chemistry To Predict Experimental Solvent Effects On Reaction Rates)

    45-46页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting from Cambridge, Massachusetts, by NewsRx journalists, research stated, “Fast and accurate prediction of solvent effects on reaction rates are crucial for kinetic modeling, chemical process design, and high-throughput solvent screening. Despite the recent advance in machine learning, a scarcity of reliable data has hindered the development of predictive models that are generalizable for diverse reactions and solvents.” Financial support for this research came from Eni S.p.A.. The news correspondents obtained a quote from the research from the Massachusetts Institute of Technology, “In this work, we generate a large set of data with the COSMO-RS method for over 28 000 neutral reactions and 295 solvents and train a machine learning model to predict the solvation free energy and solvation enthalpy of activation (Delta Delta G double dagger solv, Delta Delta H double dagger solv) for a solution phase reaction. On unseen reactions, the model achieves mean absolute errors of 0.71 and 1.03 kcal mol-1 for Delta Delta G double dagger solv and Delta Delta H double dagger solv, respectively, relative to the COSMO-RS calculations. The model also provides reliable predictions of relative rate constants within a factor of 4 when tested on experimental data. The presented model can provide nearly instantaneous predictions of kinetic solvent effects or relative rate constants for a broad range of neutral closed-shell or free radical reactions and solvents only based on atom-mapped reaction SMILES and solvent SMILES strings.”
      原文链接:
    • NETL
    • NSTL
    • Royal Soc Chemistry

    New Robotics Research from Fudan University Outlined (Continual Reinforcement Learning for Quadruped Robot Locomotion)

    46-47页
    查看更多>>摘要：Data detailed on robotics have been presented. According to news reporting out of Shanghai, People's Republic of China, by NewsRx editors, research stated, “The ability to learn continuously is crucial for a robot to achieve a high level of intelligence and autonomy.” Funders for this research include National Science And Technology Innovation 2030; Nsfc General Program. The news editors obtained a quote from the research from Fudan University: “In this paper, we consider continual reinforcement learning (RL) for quadruped robots, which includes the ability to continuously learn sub-sequential tasks (plasticity) and maintain performance on previous tasks (stability). The policy obtained by the proposed method enables robots to learn multiple tasks sequentially, while overcoming both catastrophic forgetting and loss of plasticity. At the same time, it achieves the above goals with as little modification to the original RL learning process as possible. The proposed method uses the Piggyback algorithm to select protected parameters for each task, and reinitializes the unused parameters to increase plasticity. Meanwhile, we encourage the policy network exploring by encouraging the entropy of the soft network of the policy network.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Studies from U.S. Geological Survey (USGS) Further Understanding of Machine Learning (Using Explainable Machine Learning Methods To Evaluate Vulnerability and Restoration Potential of Ecosystem State Transitions)

    47-48页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting from La Crosse, Wisconsin, by NewsRx journalists, research stated, “Ecosystem state transitions can be ecologically devastating or be a restoration success. State transitions are common within aquatic systems worldwide, especially considering human-mediated changes to land use and water use.” Financial supporters for this research include Upper Mississippi River Restoration Program, administered by the U.S. Army Corps of Engineer and the U.S. Geological Survey, U.S. Army Corps of Engineers' Upper Mississippi River Restoration Program, Winona, Minnesota. The news correspondents obtained a quote from the research from U.S. Geological Survey (USGS), “We created a transferable conceptual framework to enable multiscale assessments of state resilience and early warnings of state transitions that can inform strategic restorations and avoid ecosystem collapse. The conceptual framework integrated machine learning predictions with ecosystem state concepts (e.g., state classification, gradients of vulnerability, and recovery potential leading to state transitions) and was devised to investigate possible environmental drivers. As an application of the framework, we generated prediction probabilities of submersed aquatic vegetation (SAV) presence at nearly 10,000 sites in the Upper Mississippi River (United States). Then, we used an interpretability method to explain model predictions to gain insights into possible environmental drivers and thresholds or linear responses of SAV presence and absence. Model accuracy was 89% without spatial bias. Average water depth, suspended solids, substrate, and distance to nearest SAV were the best predictors and likely environmental drivers of SAV habitat suitability. These environmental drivers exhibited nonlinear, threshold-type responses for SAV. All the results are also presented in an online dashboard to explore results at many spatial scales.”
      原文链接:
    • NETL
    • NSTL
    • Wiley