查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting out of Esch-Sur-Alzette, Luxembourg, by NewsRx editors, research stated, "Python has become the prime language for application development in the data science and machine learning domains. However, data scientists are not necessarily experienced programmers." Our news journalists obtained a quote from the research from the University of Luxembourg, "Although Python lets them quickly implement their algorithms, when moving at scale, computation efficiency becomes inevitable. Thus, harnessing high-performance devices such as multi-core processors and graphical processing units to their potential is generally not trivial. The present narrative survey can be thought of as a reference document for such practitioners to help them make their way in the wealth of tools and techniques available for the Python language. Our document revolves around user scenarios, which are meant to cover most situations they may face." According to the news editors, the research concluded: "We believe that this document may also be of practical use to tool developers, who may use our work to identify potential lacks in existing tools and help them motivate their contributions."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Robotics are discussed in a new report. According to news reporting originating from Dalian, People's Republic of China, by NewsRx correspondents, research stated, "The trade-off between exploration and exploitation has been one of the main challenges for ensuring sampling efficiency, optimal solution, and transferability of reinforcement learning. Based on the Go-Explore framework, which is currently the most effective framework for the environments with sparse reward, latent go-explore (LGE) can overcome the complexity of manually designing state features." Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from the Dalian University of Technology, "However, its state feature space is not effective enough for measuring the sampling density, and the exploration mode with a single state as a unit is inefficient. To this end, this paper proposes the LGE with the state area as a unit, named ALGE, which can encode the real environment distance into the state feature space and realize the exploration mode with the state area as a unit to further improve exploring efficiency. The proposed ALGE is verified by a series of experiments in multiple hard-exploration environments including a continuous-maze environment, a robot environment and two Atari environments."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligence is the subject of a report. According to news originating from Bad Oeynhausen, Germany, by NewsRx correspondents, research stated, "The present study evaluated with myocardial perfusion SPECT (MPS) the diagnostic accuracy of an artificial intelligenceenabled vectorcardiography system (Cardisiography, CSG) for detection of perfusion abnormalities. We studied 241 patients, 155 with suspected CAD and 86 with known CAD who were referred for MPS." Our news journalists obtained a quote from the research from the Institute of Radiology, "The CSG was performed after the MPS acquisition. The CSG results (1) p-factor (perfusion, 0: normal, 1: mildly, 2: moderately, 3: highly abnormal) and (2) s-factor (structure, categories as p-factor) were compared with the MPS scores. The CSG system was not trained during the study. Considering the p-factor alone, a specificity of >78% and a negative predictive value of mostly >90% for all MPS variables were found. The sensitivities ranged from 17 to 56%, the positive predictive values from 4 to 38%. Combining the pand the s-factor, significantly higher specificity values of about 90% were reached. The s-factor showed a significant correlation (p=0.006) with the MPS ejection fraction. The CSG system is able to exclude relevant perfusion abnormalities in patients with suspected or known CAD with a specificity and a negative predictive value of about 90% combining the p- and the s-factor."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is the subject of a report. According to news reporting originating from Changsha, People's Republic of China, by NewsRx correspondents, research stated, "Upper gastrointestinal (GI) bleeding is a common medical emergency. This study aimed to develop models to predict critically ill patients with upper GI bleeding in-hospital and 30-day survival, identify the correlation factor and infer the causality." Our news editors obtained a quote from the research from Central South University, "A total of 2898 patients with upper GI bleeding were included from the Medical Information Mart for Intensive Care-IV and eICU-Collaborative Research Database, respectively. To identify the most critical factors contributing to the prognostic model, we used SHAP (SHapley Additive exPlanations) for machine learning interpretability. We performed causal inference using inverse probability weighting for survival-associated prognostic factors. The optimal model using the light GBM (gradient boosting algorithm) algorithm achieved an AUC of .93 for in-hospital survival, .81 for 30-day survival in internal testing and .87 for in-hospital survival in external testing. Important factors for in-hospital survival, according to SHAP, were SOFA (Sequential organ failure assessment score), GCS (Glasgow coma scale) motor score and length of stay in ICU (Intensive critical care). In contrast, essential factors for 30-day survival were SOFA, length of stay in ICU, total bilirubin and GCS verbal score. Our model showed improved performance compared to SOFA alone. Our interpretable machine learning model for predicting in-hospital and 30-day mortality in critically ill patients with upper gastrointestinal bleeding showed excellent accuracy and high generalizability."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligence is the subject of a report. According to news reporting originating in Pula, Croatia, by NewsRx journalists, research stated, "This study surveyed 1041 people in the Czech Republic to determine how well they could differentiate between news articles created by humans and those created by artificial intelligence (AI). It also explored attitudes toward AI-generated audio recordings and the future of journalism with AI." Financial support for this research came from Technology Agency of the Czech Republic under the ETA Programme. The news reporters obtained a quote from the research from the Juraj Dobrila University of Pula, "The study found that gender, age, and socioeconomic status were significant factors in how well respondents recognized the source of the text. Females were better at identifying human-generated texts, while males at identifying AI-generated texts. Younger respondents were generally more adept at recognizing AI-generated texts, education and income levels were also found to be correlated with better accuracy. Attitudes toward AI in journalism varied with age, with the 18-29 age group displaying ambivalence, the 30-49 age group being uncertain, the 50-69 age group having diverse attitudes, and the 70+ age group being skeptical. Males were more optimistic about AI's potential in journalism than females, especially among older age groups. The study's findings highlight the need for targeted digital literacy interventions tailored to different demographic groups. It provides insights into the development of digital literacy and the readiness of the population to use automated information outputs."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Machine Learning. According to news reporting originating in Xi'an, People's Republic of China, by NewsRx journalists, research stated, "The presence of mesoscale structures in gas-solid flows significantly complicates the constitutive relationship of the gassolid drag force in coarse-grid simulations. This study employs artificial neural networks to evaluate the performance of various filtered quantities in predicting the mesoscale drag force." Financial supporters for this research include Shaanxi Creative Talents Promotion Plan-Technological Innovation Team, National Natural Science Foundation of China (NSFC), Shaanxi Creative Talents Promotion Plan-Technological Innovation Team, Fundamental Research Funds for the Central Universities, HPC Platform, Xi'an Jiaotong University. The news reporters obtained a quote from the research from Xi'an Jiaotong University, "Our findings indicate that the drag model solely relying on local filtered quantities, such as solid volume fraction, slip velocity, or gas pressure gradient force, is unable to achieve the desired level of accuracy. The consideration of neighboring solid volume fractions significantly enhances the performance of the drag model, particularly in the dilute regions. In two-dimensional systems, the solid volume fractions at the eight grids closest to the considered grid are used. Additionally, the filtered solid volume fraction gradient and the filtered solid volume fraction at a second scale present a viable alternative to replace the eight neighboring solid volume fractions."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Bacterial Infections and Mycoses is the subject of a report. According to news originating from Liaoning, People's Republic of China, by NewsRx correspondents, research stated, "Infections from multidrug-resistant (MDR) bacteria have emerged as a paramount global health concern, and the therapeutic effectiveness of current treatments is swiftly diminishing. An urgent need exists to explore innovative strategies for countering drug-resistant bacteria." Our news journalists obtained a quote from the research from the Dalian University of Technology, "Bacterial DNA gyrase, functioning as an ATP-dependent enzyme, plays a pivotal role in the intricate processes of transcription, replication, and chromosome segregation within bacterial DNA. This renders it a prime target for the development of innovative antibacterial agents. However, the experimental identification of bacterial DNA gyrase inhibitors faces multifaceted challenges due to current methodological constraints. Recognizing its significance, this study developed 56 computational models designed for predicting bacterial DNA gyrase inhibitors. These models employed seven distinct molecular fingerprints and eight machine learning algorithms. Among these models, Model_2D, created using KlekotaRoth fingerprints and the SVM algorithm, stands out as the most robust performer (ACC = 0.86, MCC = 0.63, G-mean = 0.82). Moreover, given the limited exploration of structural fragments required for DNA Gyrase B inhibitors, crucial structural fingerprints influencing DNA Gyrase B inhibitors were identified through Bayesian classification. Subsequently, we conducted molecular docking to reveal the binding modes between these crucial structural fingerprints and the active site of DNA gyrase B."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on artificial intelligence. According to news reporting out of Oslo, Norway, by NewsRx editors, research stated, "In this study, we explore the potential of using functional near-infrared spectroscopy (fNIRS) signals in conjunction with modern machine-learning techniques to classify specific anatomical movements to increase the number of control commands for a possible fNIRS-based brain-computer interface (BCI) applications. The study focuses on novel individual finger-tapping, a well-known task in fNIRS and fMRI studies, but limited to left/right or few fingers." The news reporters obtained a quote from the research from Oslo Metropolitan University (OsloMet): "Twenty-four right-handed participants performed the individual finger-tapping task. Data were recorded by using sixteen sources and detectors placed over the motor cortex according to the 10-10 international system. The event's average oxygenated D HbO and deoxygenated D HbR hemoglobin data were utilized as features to assess the performance of diverse machine learning (ML) models in a challenging multi-class classification setting. These methods include LDA, QDA, MNLR, XGBoost, and RF. A new DL-based model named ‘Hemo-Net' has been proposed which consists of multiple parallel convolution layers with different filters to extract the features. This paper aims to explore the efficacy of using fNRIS along with ML/DL methods in a multi-class classification task. Complex models like RF, XGBoost, and Hemo-Net produce relatively higher test set accuracy when compared to LDA, MNLR, and QDA. Hemo-Net has depicted a superior performance achieving the highest test set accuracy of 76%, however, in this work, we do not aim at improving the accuracies of models rather we are interested in exploring if fNIRS has the neural signatures to help modern ML/DL methods in multi-class classification which can lead to applications like brain-computer interfaces. Multi-class classification of fine anatomical movements, such as individual finger movements, is difficult to classify with fNIRS data."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Artificial Intelligence. According to news originating from Wuhan, People's Republic of China, by NewsRx correspondents, research stated, "Artificial intelligence (AI) and robots have the potential to revolutionize society, with impacts ranging from the broadest reaches of industry and policy to the minutiae of daily life. The extent to which AI-based technologies can bring benefits to human society depends on how people perceive them–folk beliefs of AI and robots." Funders for this research include National Office of Philosophy and Social Sciences, National Natural Science Foundation of China (NSFC).
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artificial intelligence. According to news reporting from Macau, People's Republic of China, by NewsRx journalists, research stated, "Organic crystal structures exert a profound impact on the physicochemical properties and biological effects of organic compounds." Funders for this research include University of Macau. The news correspondents obtained a quote from the research from University of Macau: "Quantum mechanics (QM)-based crystal structure predictions (CSPs) have somewhat alleviated the dilemma that experimental crystal structure investigations struggle to conduct complete polymorphism studies, but the high computing cost poses a challenge to its widespread application. The present study aims to construct DeepCSP, a feasible pure machine learning framework for minute-scale rapid organic CSP. Initially, based on 177,746 data entries from the Cambridge Crystal Structure Database, a generative adversarial network was built to conditionally generate trial crystal structures under selected feature constraints for the given molecule. Simultaneously, a graph convolutional attention network was used to predict the density of stable crystal structures for the input molecule. Subsequently, the distances between the predicted density and the definition-based calculated density would be considered to be the crystal structure screening and ranking basis, and finally, the density-based crystal structure ranking would be output. Two such distinct algorithms, performing the generation and ranking functionalities, respectively, collectively constitute the DeepCSP, which has demonstrated compelling performance in marketed drug validations, achieving an accuracy rate exceeding 80% and a hit rate surpassing 85%."
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