查看更多>>摘要：Fresh data on Machine Learning are presented in a new report. According to news reporting out of Prague, Czech Republic, by NewsRx editors, research stated, “Soil organic carbon (SOC) is an important soil characteristic as well as a way how to mitigate climate change. Information on its content and spatial distribution is thus crucial.” Funders for this research include Czech University of Life Sciences Prague, Prague, Technology Agency of the Czech Republic, Ministry of Agriculture, Czech Republic. Our news journalists obtained a quote from the research from the Czech University of Life Sciences Prague, “Digital soil mapping (DSM) is a suitable way to evaluate spatial distribution of soil properties thanks to its ability to obtain accurate information about soil. This research aims to apply machine learning algorithms using various environmental covariates to generate digital SOC maps for mineral topsoils in the Liberec and Domazlice districts, located in the Czech Republic. The soil class, land cover, and geology maps as well as terrain covariates extracted from the digital elevation model and remote sensing data were used as covariates in modelling. The spatial distribution of SOC was predicted based on its relationships with covariates using random forest (RF), cubist, and quantile random forest (QRF) models. Results of the RF model showed that land cover (vegetation) and elevation were the most important environmental variables in the SOC prediction in both districts. The RF had better efficiency and accuracy than the cubist and QRF to predict SOC in both districts. The greatest R2 value (0.63) was observed in the Domazlice district using the RF model.”
查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting from Ljubljana, Slovenia, by NewsRx journalists, research stated, “The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare providers, regulatory authorities and payers, screening programmes need to show that the testing variables allow accurate risk prediction and that individualised risk-informed monitoring plans are established, as well as operational feasibility, cost-effectiveness and acceptance at population level.” Financial supporters for this research include VINNOVA, Lund University. The news correspondents obtained a quote from the research from University Medical Center Ljubljana, “Artificial intelligence (AI) has the potential to contribute to solving these issues, starting with the identification and stratification of at-risk individuals. ASSET (AI for Sustainable Prevention of Autoimmunity in the Society; www.asset.healthcare ) is a public/private consortium that was established to contribute to research around screening for type 1 diabetes and particularly to how AI can drive the implementation of a precision medicine approach to disease prevention. ASSET will additionally focus on issues pertaining to operational implementation of screening. The authors of this article, researchers and clinicians active in the field of type 1 diabetes, met in an open forum to independently debate key issues around screening for type 1 diabetes and to advise ASSET. The potential use of AI in the analysis of longitudinal data from observational cohort studies to inform the design of improved, more individualised screening programmes was also discussed. A key issue was whether AI would allow the research community and industry to capitalise on large publicly available data repositories to design screening programmes that allow the early detection of individuals at high risk and enable clinical evaluation of preventive therapies. Overall, AI has the potential to revolutionise type 1 diabetes screening, in particular to help identify individuals who are at increased risk of disease and aid in the design of appropriate follow-up plans.”
查看更多>>摘要：Investigators publish new report on Machine Learning - Intelligent Systems. According to news reporting originating from Qingdao, People’s Republic of China, by NewsRx correspondents, research stated, “The intelligent fault diagnosis model has made a significant development, whose highprecision results rely on a large amount of labeled data. However, in the actual industrial environment, it is very difficult to obtain a large amount of labeled data.” Financial support for this research came from Science and Technology Innovation 2025 Major Project of Ningbo. Our news editors obtained a quote from the research from the China University of Petroleum, “It will make it difficult for the fault diagnosis model to converge with limited labeled industrial data. To address this paradox, we propose a novel unsupervised domain adaptation framework (M-Net) for fault diagnosis of rotating machinery, which only requires unlabeled industrial data. The M-Net will be pretrained using the labeled data, which can be accessed through the labs. In this stage, we propose a multi-scale feature extractor that can extract and fuse multi-scale features. This operation will generalize the features further. Then, we will align the distribution of the labeled data and unlabeled industrial data using the generator model based on multi-kernel maximum mean discrepancy. This will reduce the distribution distance between the labeled data and the unlabeled industrial data. For now, the unsupervised domain adaptation problem has shifted to a semi-supervised domain adaptation problem.”
查看更多>>摘要：Investigators publish new report on Machine Translation. According to news originating from Nanjing, People’s Republic of China, by NewsRx correspondents, research stated, “The scarcity of bilingual parallel corpus imposes limitations on exploiting the state-of-the-art supervised translation technology. One of the research directions is employing relations among multi-modal data to enhance performance.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Southeast University, “However, the reliance on manually annotated multi-modal datasets results in a high cost of data labeling. In this paper, the topic semantics of images is proposed to alleviate the above problem. First, topic-related images can be automatically collected from the Internet by search engines. Second, topic semantics is sufficient to encode the relations between multi-modal data such as texts and images. Specifically, we propose a visual topic semantic enhanced translation (VTSE) model that utilizes topic-related images to construct a cross-lingual and cross-modal semantic space, allowing the VTSE model to simultaneously integrate the syntactic structure and semantic features. In the above process, topic similar texts and images are wrapped into groups so that the model can extract more robust topic semantics from a set of similar images and then further optimize the feature integration. The results show that our model outperforms competitive baselines by a large margin on the Multi30k and the Ambiguous COCO datasets.”
查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting from Tempe, Arizona, by NewsRx journalists, research stated, “The recent surge of machine learning (ML) has impacted many disciplines, including educational and psychological measurement (hereafter shortened as measurement). The measurement literature has seen rapid growth in applications of ML to solve measurement problems.” The news correspondents obtained a quote from the research from Arizona State University, “However, as we emphasize in this article, it is imperative to critically examine the potential risks associated with involving ML in measurement. The MxML project aims to explore the relationship between measurement and ML, so as to identify and address the risks and better harness the power of ML to serve measurement missions. This paper describes the first study of the MxML project, in which we summarize the state of the field of applications, extensions, and discussions about ML in measurement contexts with a systematic review of the recent 10 years’ literature.”
查看更多>>摘要：Investigators publish new report on robotics. According to news originating from Northwestern Polytechnical University by NewsRx editors, the research stated, “The manta ray, exemplifying an agile swimming mode identified as the median and paired fin (MPF) mode, inspired the development of underwater robots. Robotic manta typically comprises a central rigid body and flexible pectoral fins.” Our news journalists obtained a quote from the research from Northwestern Polytechnical University: “Flexible fins provide excellent maneuverability. However, due to the complexity of material mechanics and hydrodynamics, its dynamics are rarely studied, which is crucial for the advanced control of robotic manta (such as trajectory tracking, obstacle avoidance, etc.). In this paper, we develop a multibody dynamic model for our novel manta robot by introducing a pseudo-rigid body (PRB) model to consider passive deformation in the spanwise direction of the pectoral fins while avoiding intricate modeling. In addressing the rigidflexible coupling dynamics between flexible fins and the actuation mechanism, we employ a sequential coupling technique commonly used in fluid-structure interaction (FSI) problems. Numerical examples are provided to validate the MPF mode and demonstrate the effectiveness of the dynamic model. We show that our model performs well in the rigid-flexible coupling analysis of the manta robot.”
查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting from Ames, Iowa, by NewsRx journalists, research stated, “Pursuit of superconductivity in light-element systems at ambient pressure is of great experimental and theoretical interest. In this work, we combine a machine learning (ML) method with first-principles calculations to efficiently search for the energetically favorable ternary Ca-B-C compounds.” Funders for this research include National Natural Science Foundation of China (NSFC), United States Department of Energy (DOE), Iowa State University. The news correspondents obtained a quote from the research from Iowa State University, “Three layered borocarbides (stable CaBC5 and metastable Ca2BC11 and CaB3C3) are predicted to be phonon-mediated superconductors at ambient pressure. The stable CaBC5 and the low-energy metastable Ca2BC11 (with formation energy only 9.5 meV/atom above the convex hull) have a superconducting T-c of 5.2 and 8.9 K, respectively. While the hexagonal CaB3C3 possesses a Tc of 26.1 K, it is metastable with formation energy of 153 meV/atom above the convex hull.”
查看更多>>摘要：Current study results on Robotics - Robotics and Automation have been published. According to news reporting originating in Changsha, People’s Republic of China, by NewsRx journalists, research stated, “Motion planning has been an important research topic in achieving safe and flexible maneuvers for intelligent vehicles. However, it remains challenging to realize efficient and optimal planning in the presence of uncertain model dynamics.” Financial support for this research came from National Natural Science Foundation of China (NSFC). The news reporters obtained a quote from the research from the National University of Defense Technology, “In this paper, a sparse kernel-based reinforcement learning (RL) algorithm with Gaussian process (GP) regression (called GP-SKRL) is proposed to realize online adaptation and near-optimal motion planning performance. In this algorithm, we design an efficient sparse GP regression method to learn the uncertain dynamics. Based on the updated model, a sparse kernel-based policy iteration algorithm with an exponential barrier function is designed to learn the near-optimal planning policies with the capability to avoid dynamic obstacles. Thereby, batch-mode GP-SKRL with online adaption capability can estimate the changing system dynamics. The converged RL policies are then deployed on vehicles efficiently under a safety-aware module. As a result, the produced driving actions are safe and less conservative, and the planning performance has been noticeably improved. Extensive simulation results show that GP-SKRL outperforms several advanced motion planning methods in terms of average cumulative cost, trajectory length, and task completion time.”
查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting from Changsha, People’s Republic of China, by NewsRx journalists, research stated, “To address the issues surrounding incomplete coverage of core dictionaries, limited training corpora, and low precision in Chinese geological text segmentation, a knowledge-and data-driven word segmentation method by combining combination probability and machine learning was proposed in this paper. We extracted mathe-matical feature information from terms in Chinese geological text to construct a Term Combination Probability Model (TCPM) for Chinese word combinations by integrating the combination features of geological terms and the Chinese writing styles under zero-sample conditions.” Financial support for this research came from National Natural Science Foundation of China (NSFC). The news correspondents obtained a quote from the research from the School of Geosciences and Info-Physics, “The TCPM was used to extract geological terms with high combination characteristics as a user-defined dictionary, and then a geological corpus was constructed by using a general domain word segmentation method based on this dictionary. After a small amount of manual review and optimization, the geological corpus was trained with a BiLSTM-CRF model to segment Chinese geological text. The proposed method in this paper was tested using a regional geological survey report set in Henan Province, and the precision, recall, and F1-score of the method are 92.65%, 92.53%, and 92.59%, respectively.”
查看更多>>摘要：A new study on Symbolic Computation is now available. According to news reporting out of Moscow, Russia, by NewsRx editors, research stated, “The study of formal stability of equilibrium positions of a multiparametric Hamiltonian system in a generic case is traditionally carried out using its normal form under the condition of the absence of resonances of small orders. In this paper we propose a method of symbolic computation of the condition of existence of a resonance of arbitrary order for a system with three degrees of freedom.” Our news journalists obtained a quote from the research from the Russian Academy of Sciences, “It is shown that this condition for each resonant vector can be represented as a rational algebraic curve. By methods of computer algebra the rational parametrization of this curve for the case of an arbitrary resonance is obtained.” According to the news editors, the research concluded: “A model example of some two-parameter system of pendulum type is considered.”
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