期刊,Robotics & Machine Learning Daily News 2024年卷Feb.22期_国家学术搜索

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Robotics & Machine Learning Daily News

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State Key Laboratory Reports Findings in Machine Learning (Pre- dicting the enthalpy of formation of energetic molecules via con- ventional machine learning and GNN)

68-68页

查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news originating from Beijing, People's Republic of China, by NewsRx correspondents, research stated, "Machine learning (ML) provides a promising method for efficiently and accurately predicting molecular properties. Using ML models to predict the enthalpy of formation of energetic molecules helps in fast screening of potential high-energy molecules, thereby accelerating the design of energetic materials." Financial support for this research came from State Key Laboratory of Explosion Science and Technol- ogy. Our news journalists obtained a quote from the research from State Key Laboratory, "A persistent challenge is to determine the optimal featurization methods for molecular representation and use an ap- propriate ML model. Thus, in our study, we evaluate various featurization methods (CDS, ECFP, SOAP, GNF) and ML models (RF, MLP, GCN, MPNN), dividing them into two groups: conventional ML models and GNN models, to predict the enthalpy of formation of potential high-energy molecules. Our results demonstrate that CDS and SOAP have advantages over the ECFP, while the GNFs in GCN and MPNN models perform better. Furthermore, the MPNN model performs best among all models with a root mean square error (RMSE) as low as 8.42 kcal mol, surpassing even the best performing CDS-MLP model in conventional ML models."

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Royal Soc Chemistry

Zhongnan Hospital of Wuhan University Reports Findings in Heart Failure (Non-contact assessment of cardiac physiology using FO- MVSS-based ballistocardiography: a promising approach for heart failure evaluation)

69-70页

查看更多>>摘要：New research on Heart Disorders and Diseases - Heart Failure is the subject of a report. According to news reporting from Hubei, People's Republic of China, by NewsRx journalists, research stated, "Continuous monitoring of cardiac motions has been expected to provide essential cardiac physiology information on cardiovascular functioning. A fiber-optic micro-vibration sensing system (FO- MVSS) makes it promising." Financial support for this research came from National Natural Science Foundation of China. The news correspondents obtained a quote from the research from the Zhongnan Hospital of Wuhan University, "This study aimed to explore the correlation between Ballistocardiography (BCG) waveforms, measured using an FO-MVSS, and myocardial valve activity during the systolic and diastolic phases of the cardiac cycle in participants with normal cardiac function and patients with congestive heart failure (CHF). A high-sensitivity FO-MVSS acquired continuous BCG recordings. The simultaneous recordings of BCG and electrocardiogram (ECG) signals were obtained from 101 participants to examine their correlation. BCG, ECG, and intracavitary pressure signals were collected from 6 patients undergoing cardiac catheter intervention to investigate BCG waveforms and cardiac cycle phases. Tissue Doppler imaging (TDI) mea- sured cardiac time intervals in 51 participants correlated with BCG intervals. The BCG recordings were further validated in 61 CHF patients to assess cardiac parameters by BCG. For heart failure evaluation machine learning was used to analyze BCG-derived cardiac parameters. Significant correlations were ob- served between cardiac physiology parameters and BCG's parameters. Furthermore, a linear relationship was found betwen IJ amplitude and cardiac output (r = 0.923, R = 0.926, p＜0.001). Machine learning techniques, including K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC), Support Vector Ma- chine (SVM), Logistic Regression (LR), Random Forest (RF), and XGBoost, respectively, demonstrated remarkable performance. They all achieved average accuracy and AUC values exceeding 95% in a five-fold cross-validation approach. We establish an electromagnetic-interference-free and non-contact method for continuous monitoring of the cardiac cycle and myocardial contractility and measure the different phases of the cardiac cycle."

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New Machine Learning Study Results from New York University (NYU) Described (Visual Exploration of Machine Learning Model Behavior With Hierarchical Surrogate Rule Sets)

70-71页

查看更多>>摘要：Researchers detail new data in Machine Learning. According to news reporting origi- nating in New York City, New York, by NewsRx journalists, research stated, "One of the potential solutions for model interpretation is to train a surrogate model: a more transparent model that approximates the behavior of the model to be explained. Typically, classification rules or decision trees are used due to their logic-based expressions." Financial support for this research came from Capital One Financial Corporation. The news reporters obtained a quote from the research from New York University (NYU), "However, decision trees can grow too deep, and rule sets can become too large to approximate a complex model. Unlike paths on a decision tree that must share ancestor nodes (conditions), rules are more flexible. However, the unstructured visual representation of rules makes it hard to make inferences across rules. In this paper, we focus on tabular data and present novel algorithmic and interactive solutions to address these issues. First, we present Hierarchical Surrogate Rules (HSR), an algorithm that generates hierarchical rules based on user-defined parameters. We also contribute SuRE, a visual analytics (VA) system that integrates HSR and an interactive surrogate rule visualization, the Feature-Aligned Tree, which depicts rules as trees while aligning features for easier comparison. We evaluate the algorithm in terms of parameter sensitivity, time performance, and comparison with surrogate decision trees and find that it scales reasonably well and overcomes the shortcomings of surrogate decision trees. We evaluate the visualization and the system through a usability study and an observational study with domain experts. Our investigation shows that the participants can use feature-aligned trees to perform non-trivial tasks with very high accuracy."

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IEEE

Researchers at Southwest Petroleum University Release New Data on Support Vector Machines (Improved Least Squares Support Vec- tor Machine Model Based On Grey Wolf Optimizer Algorithm for Predicting Co2-crude Oil Minimum Miscibility ...)

71-71页

查看更多>>摘要：2024 FEB 22 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Researchers detail new data in Support Vector Machines. According to news reporting originating from Chengdu, People's Republic of China, by NewsRx correspondents, research stated, "The minimum miscibility pressure (MMP) is an important reference parameter in the study of CO2 oil drive systems. In response to the problems of time-consuming and costly prediction of MMP by conventional experimental methods, an improved least squares support vector machine (LSSVM) model based on grey wolf optimizer (GWO) algorithm is proposed to predict the CO2-crude oil MMP." Our news editors obtained a quote from the research from Southwest Petroleum University, "Based on Pearson correlation analysis, reservoir temperature, C5+ molecular weight, intermediate component mole fraction, and volatile component mole fraction are selected as independent variables of the model, and MMP is the dependent variable. A total of 51 MMP experimental data are collected, of which 35 are used to fine-tune the model's parameters and 16 are used to verify the model's reliability. The high leverage point method is used to detect anomalies in all experimental data to check the reliability of the model, and the abnormality of only one piece of experimental data is identified. Finally, a comparison of the model with other intelligent models is found."

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Wiley

Recent Studies from Shandong University of Science and Technol- ogy Add New Data to Machine Learning (Efficient Identification of Crude Oil via Combined Terahertz Time-Domain Spectroscopy and Machine Learning)

72-72页

查看更多>>摘要：Research findings on artificial intelligence are discussed in a new report. According to news originating from Qingdao, People's Republic of China, by NewsRx editors, the research stated, "The quality of crude oil varies significantly according to its geographical origin." Funders for this research include National Key R&D Program of China; National Natural Science Foundation of China; China Postdoctoral Science Foundation. The news reporters obtained a quote from the research from Shandong University of Science and Technology: "The efficient identification of the source region of crude oil is pivotal for petroleum trade and processing. However, current methods, such as mass spectrometry and fluorescence spectroscopy, suffer problems such as complex sample preparation and a long characterization time, which restrict their efficiency. In this work, by combining terahertz time-domain spectroscopy (THz-TDS) and a machine learning analysis of the spectra, an efficient workflow for the accurate and fast identification of crude oil was established."

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Mdpi

New Robotics Research from All India Institute of Medical Sciences (AIIMS) Outlined (Robot-assisted ureteric reconstructive surgeries for benign diseases: Initial single-center experience with point of technique)

72-73页

查看更多>>摘要：Fresh data on robotics are presented in a new report. According to news reporting out of Rajasthan, India, by NewsRx editors, research stated, "We present our initial experience with robot-assisted reconstructive surgeries with the Da Vinci Xi robotic system for benign ureteric pathologies. This is a retrospective review of prospectively collected data of patients who underwent robot-assisted reconstructive procedures for benign diseases of the ureter at our department from April 2018 to November 2022." The news correspondents obtained a quote from the research from All India Institute of Medical Sci- ences (AIIMS): "Demographic and perioperative details were recorded. Patients were followed up and surgical success was evaluated on the basis of symptomatic, functional, and radiological improvement. A total of 34 patients underwent robot-assisted reconstructions for benign ureteric pathologies by various techniques. Mean age, body mass index (BMI), hospital stay and follow-up duration were 36 years, 24.1 kg/m~2 , 5.29 days, and 7.08 months respectively. Procedures included pyeloplasty in eight, primary uretero- neocystostomy (UNC) in seven, Psoas hitch UNC in five, Boari flap UNC in six, Ureteroureterostomy in four, ureterocalicostomy in two and ileal ureteral transposition in two patients. Mean docking time, total operative time, and estimated blood loss were 31.5 min, 178 min, and 64.3 ml, respectively. All patients had radiologic or functional improvement on follow-up after 6 months."

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Sage

Recent Findings from University of Edinburgh Provides New In- sights into Machine Learning (Use of Machine Learning Models To Warmstart Column Generation for Unit Commitment)

73-74页

查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting originating from Edinburgh, United Kingdom, by NewsRx correspondents, research stated, "The unit commitment problem is an important optimization problem in the energy industry used to compute the most economical operating schedules of power plants. Typically, this problem has to be solved repeatedly with different data but with the same problem structure." Our news editors obtained a quote from the research from the University of Edinburgh, "Machine learning techniques have been applied in this context to find primal feasible solutions. Dantzig-Wolfe de- composition with a column generation procedure is another approach that has been shown to be successful in solving the unit commitment problem to tight tolerance. We propose the use of machine learning models not to find primal feasible solutions directly but to generate initial dual values for the column generation procedure. Our numerical experiments compare machine learning-based methods for warmstarting the col- umn generation procedure with three baselines: column prepopulation, the linear programming relaxation, and coldstart. The experiments reveal that the machine learning approaches are able to find both tight lower bounds and accurate primal feasible solutions in a shorter time compared with the baselines."

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Informs

Report Summarizes Machine Learning Study Findings from Univer- sity of Nicosia (Unsupervised Machine Learning of Virus Dispersion Indoors)

74-75页

查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting originating from Nicosia, Cyprus, by NewsRx correspondents, research stated, "This paper con- cerns analyses of virus droplet dynamics resulting from coughing events within a confined environment using, as an example, a typical cruiser's cabin. It is of paramount importance to be able to comprehend and predict droplet dispersion patterns within enclosed spaces under varying conditions." Funders for this research include HORIZON EUROPE Framework Programme10.13039/100018693, European Union's Horizon Europe Research and Innovation Actions program. Our news editors obtained a quote from the research from the University of Nicosia, "Numerical simula- tions are expensive and difficult to perform in real-time situations. Unsupervised machine learning methods are proposed to study droplet dispersion patterns. Data from multi-phase computational fluid dynamics simulations of coughing events at different flow rates are utilized with an unsupervised learning algorithm to identify prevailing trends based on the distance traveled by the droplets and their sizes. The algorithm determines optimal clustering by introducing novel metrics such as the Clustering Dominance Index and Uncertainty. Our analysis revealed the existence of three distinct stages for droplet dispersion during a coughing event, irrespective of the underlying flow rates. An initial stage where all droplets disperse homo- geneously, an intermediate stage where larger droplets overtake the smaller ones, and a final stage where the smaller droplets overtake the larger ones."

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Studies from University of Chicago Provide New Data on Machine Learning (Gravity Spy: Lessons Learned and a Path Forward)

75-76页

查看更多>>摘要：2024 FEB 22 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Research findings on Machine Learning are discussed in a new report. According to news reporting from Chicago, Illinois, by NewsRx journalists, research stated, "The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data. By using both the work of citizen-science volunteers and machine learning algorithms, the Gravity Spy project enables reliable classification of glitches." Financial supporters for this research include National Science Foundation (NSF), Google Incorporated, Alfred P. Sloan Foundation, National Science Foundation (NSF), NASA through the NASA Hubble Fel- lowship, Space Telescope Science Institute, National Aeronautics & Space Administration (NASA), CIERA Board of Visitors Research Professorship, Canadian Institute for Advanced Research (CIFAR), Daniel I. Linzer Distinguished University Professorship fund, Science and Technology Development Fund (STDF), National Science Foundation (NSF), LIGO Laboratory, NSF's LIGO Laboratory - National Science Founda- tion, Office of the Provost, Office for Research, Northwestern University Information Technology, National Science Foundation (NSF).

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Springer Nature

Dalian Medical University Reports Findings in Head and Neck Cancer (Quantified pathway mutations associate epithelial- mesenchymal transition and immune escape with poor prognosis and immunotherapy resistance of head and neck squamous cell ...)

76-77页

查看更多>>摘要：New research on Oncology - Head and Neck Cancer is the subject of a report. Ac- cording to news reporting from Dalian, People's Republic of China, by NewsRx journalists, research stated, "Pathway mutations have been calculated to predict the poor prognosis and immunotherapy resistance in head and neck squamous cell carcinoma (HNSCC). To uncover the unique markers predicting prognosis and immune therapy response, the accurate quantification of pathway mutations are required to evaluate epithelial-mesenchymal transition (EMT) and immune escape." Financial support for this research came from National Natural Science Fundation of China. The news correspondents obtained a quote from the research from Dalian Medical University, "Yet, there is a lack of score to accurately quantify pathway mutations. Firstly, we proposed Individualized Weighted Hallmark Gene Set Mutation Burden which integrated pathway structure information and eliminated the interference of global Tumor Mutation Burden to accurately quantify pathway mutations. Subsequently, to further elucidate the association of IWHMB with EMT and immune escape, support vector machine regression model was used to identify IWHMB- related transcriptomic features (IRG), while Adversarially Regularized Graph Autoencoder (ARVGA) was used to further resolve IRG network features. Finally, Random walk with restart algorithm was used to identify biomarkers for predicting ICI response. We quantified the HNSCC pathway mutation signatures and identified pathway mutation subtypes using IWHMB. The IWHMB-related transcriptomic features (IRG) identified by support vector machine regression were divided into 5 communities by ARVGA, among which the Community 1 enriching malignant mesenchymal components promoted EMT dynamically and regulated immune patterns associated with ICI responses. Bridge Hub Gene (BHG) identified by random walk with restart was key to IWHMB in EMT and immune escape, thus, more predictive for ICI response than other 70 public signatures."

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Springer Nature